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Amin, M. Miftakul, and Yevi Dwitayanti. "Komparasi Kinerja Algoritma Blocking Pada Proses Indexing Untuk Deteksi Duplikasi." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 4 (2024): 715–22. http://dx.doi.org/10.25126/jtiik.1148080.
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Proses integrasi data dari heterogeneous data sources memerlukan kualitas data yang baik. Salah satu ciri kualitas data yang baik adalah terhindar dari terjadinya duplikasi data. Untuk melakukan deteksi duplikasi, langkah yang dapat dilakukan adalah membandingkan setiap record dalam sebuah dataset sehingga membentuk candidate record pair. Teknik blocking digunakan untuk proses indexing yang dapat mengurangi jumlah pasangan record dalam proses deteksi duplikasi. Penelitian ini bertujuan untuk melakukan perbandingan beberapa algoritma blocking sehingga diperoleh rekomendasi algoritma mana yang paling optimal digunakan. Penelitian ini melakukan investigasi terhadap 6 buah algoritma blocking, yaitu Soundex, NYSIIS, Metaphone, Double Metaphone, Jaro Winkler Similarity, dan Cosine Similarity. Dataset yang digunakan dalam penelitian ini adalah dataset restaurant yang berisi 112 record, yang di dalamnya terdapat beberapa record yang terindikasi duplikat. Hasil penelitian menunjukkan bahwa algoritma NYSIIS memberikan hasil record blocking paling optimal, yaitu sebesar 97 record. Sedangkan algoritma Soundex dan Cosine Similarity memberikan hasil yang paling optimal, yaitu sebesar 8 buah candidate record pair. Sedangkan dari sisi waktu eksekusi algoritma Soundex dan NYSIIS memberikan proses yang paling cepat dengan durasi 0,04 detik. Abstract The process of integrating data from heterogeneous data sources requires good data quality. One of the characteristics of good data quality is avoiding data duplication. To perform duplication detection, a step that can be done is to compare each record in a dataset to form a candidate record pair. The blocking algorithm is used for the indexing process which can reduce the number of record pairs in the duplication detection process. This research aims to compare several blocking algorithms so as to obtain recommendations on which algorithm is most optimally used. This research investigates 6 blocking algorithms, namely Soundex, NYSIIS, Metaphone, Double Metaphone, Jaro Winkler Similarity, and Cosine Similarity. The dataset used in this research is a restaurant dataset containing 112 records, in which there are several records that indicate duplicates. The results showed that the NYSIIS algorithm provided the most optimal record blocking results, which amounted to 97 records. While the Soundex and Cosine Similarity algorithms provide the most optimal results, which are 8 candidate record pairs. In terms of execution time, the Soundex and NYSIIS algorithms provide the fastest process with a duration of 0.04 seconds.
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Arora, Monika, and Vineet Kansal. "The Inverse Edit Term Frequency for Informal Word Conversion Using Soundex for Analysis of Customer’s Reviews." Recent Advances in Computer Science and Communications 13, no. 5 (2020): 917–25. http://dx.doi.org/10.2174/2213275912666190405114330.
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Background: E-commerce/ M-commerce has emerged as a new way of doing businesses in the present world which requires an understanding of the customer’s needs with the utmost precision and appropriateness. With the advent of technology, mobile devices have become vital tools in today’s world. In fact, smart phones have changed the way of communication. The user can access any information on a single click. Text messages have become the basic channel of communication for interaction. The use of informal text messages by the customers has created a challenge for the business segments in terms of creating a gap pertaining to the actual requirement of the customers due to the inappropriate representation of it's need by using short message service in an informal manner. Objective: The informally written text messages have become a center of attraction for researchers to analyze and normalize such textual data. In this paper, the SMS data have been analyzed for information retrieval using Soundex Phonetic algorithm and its variations. Methods: Two datasets have been considered, SMS- based FAQ of FIRE 2012 and self-generated survey dataset have been tested for evaluating the performance of the proposed Soundex Phonetic algorithm. Results: It has been observed that by applying Soundex with Inverse Edit Term Frequency, the lexical similarity between the SMS word and Natural language text has been significantly improved. The results have been shown to prove the work. Conclusion: Soundex with Inverse Edit Term Frequency Distribution algorithm is best suited among the various variations of Soundex. This algorithm normalizes the informally written text and gets the exact match from the bag of words.
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Shah, Rima, and Dheeraj Kumar Singh. "Improvement of Soundex Algorithm for Indian Language Based on Phonetic Matching." International Journal of Computer Science, Engineering and Applications 4, no. 3 (2014): 31–39. http://dx.doi.org/10.5121/ijcsea.2014.4303.
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Christopher Jaisunder, G., Israr Ahmed, and R. K. Mishra. "Need for Customized Soundex based Algorithm on Indian Names for Phonetic Matching." Global Journal of Enterprise Information System 8, no. 2 (2017): 30. http://dx.doi.org/10.18311/gjeis/2016/7658.
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In any digitization program, the reproduction of the handwritten demographic data is a challenging job particularly for the records of previous decades. Nowadays, the requirement of the digitization of the individual’s past records becomes very much essential. In the areas like financial inclusion, border security, driving license, passport issuance, weapon license, banking sectors, health care and social welfare benefits, the individual’s earlier case history is a mandatory part of the decision making process. Documents are scanned and stored in a systematic method; each and every scanned document is tagged with a proper key. Documents are retrieved with the help of assigned key, for the purpose of data entry through the software program/ package. Here comes the difficulty that the data, particularly the critical personal data like name and father name etc., may not be legible for the reading purpose and the data entry operators type the characters as per their understanding. The chances of error is of high order in name variations in terms of duplicate characters, abbreviations, omissions, ignoring space between names and wrong spelling. Now the challenge is that, result of data retrieval over these key fields may not be proper because of the wrong data entry. We need to explore the opportunities and challenges for defining the effective strategies to execute this job without compromising the quality and quantity of the matches. In this scenario, we need to have an appropriate string matching algorithm with the phonetic matching. The algorithm is to be defined according to the nature, type and region of the data domain so that the search shall be phonetic based rather than simple string comparison. In this paper, I have tried to explain the need for customized soundex based algorithm on phonetic matching over the misspelt, incomplete, repetitive and partial prevalent data.
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Volodymyr, Buriachok, Hadzhyiev Matin, Sokolov Volodymyr, Skladannyi Pavlo, and Kuzmenko Lidiia. "IMPLANTATION OF INDEXING OPTIMIZATION TECHNOLOGY FOR HIGHLY SPECIALIZED TERMS BASED ON METAPHONE PHONETICAL ALGORITHM." Eastern-European Journal of Enterprise Technologies 5, no. 2 (101) (2019): 43–50. https://doi.org/10.15587/1729-4061.2019.181943.
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When compiling databases, for example to meet the needs of healthcare establishments, there is quite a common problem with the introduction and further processing of names and surnames of doctors and patients that are highly specialized both in terms of pronunciation and writing. This is because names and surnames of people cannot be unique, their notation is not subject to any rules of phonetics, while their length in different languages may not match. With the advent of the Internet, this situation has become generally critical and can lead to that multiple copies of e-mails are sent to one address. It is possible to solve the specified problem by using phonetic algorithms for comparing words Daitch-Mokotoff, SoundEx, NYSIIS, Polyphone, and Metaphone, as well as the Levenstein and Jaro algorithms, Q-gram-based algorithms, which make it possible to find distances between words. The most widespread among them are the SoundЕx and Metaphone algorithms, which are designed to index the words based on their sound, taking into consideration the rules of pronunciation. By applying the Metaphone algorithm, an attempt has been made to optimize the phonetic search processes for tasks of fuzzy coincidence, for example, at data deduplication in various databases and registries, in order to reduce the number of errors of incorrect input of surnames. An analysis of the most common surnames reveals that some of them are of the Ukrainian or Russian origin. At the same time, the rules following which the names are pronounced and written, for example in Ukrainian, differ radically from basic algorithms for English and differ quite significantly for the Russian language. That is why a phonetic algorithm should take into consideration first of all the peculiarities in the formation of Ukrainian surnames, which is of special relevance now. The paper reports results from an experiment to generate phonetic indexes, as well as results of the increased performance when using the formed indexes. A method for adapting the search for other areas and several related languages is presented separately using an example of search for medical preparations
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Paliulionis, Viktoras. "Lietuviškų adresų geokodavimo problemos ir jų sprendimo būdai." Informacijos mokslai 50 (January 1, 2009): 217–22. http://dx.doi.org/10.15388/im.2009.0.3235.
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Geokodavimas yra procesas, kai tekstinis vietos aprašas transformuojamas į geografi nes koordinates. Vienas iš dažniausiai naudojamų vietos aprašymo būdų yra pašto adresas, kurį sudaro gyvenvietės pavadinimas, gatvės pavadinimas, namo numeris ir kiti adreso elementai. Šiame straipsnyje nagrinėjamos lietuviškų adresų geokodavimo problemos, atsirandančios dėl adreso formatų įvairovės, netiksliai ir su rašybos klaidomis užrašomų adresų. Straipsnyje aprašyti geokodavimo procesoetapai ir juose naudojamų algoritmų principai. Pasiūlytas lietuvių kalbai pritaikytas LT-Soundex algoritmas, leidžiantis indeksuoti adreso elementus pagal fonetinį panašumą ir atlikti apytikslę paiešką.Lithuanian Address Geocoding: Problems and SolutionsViktoras Paliulionis SummaryGeocoding is the process of converting of a textual description of a location into geographic coordinates. One of the most frequently used way to describe a place is its postal address that contains a city name, street name, house number and other address components. The paper deals with the problems of the geocoding of Lithuanian addresses. The main problems are variety of used address formats and possible typing and spelling errors. The paper describes the steps of the geocoding process and used algorithms. We propose a phonetic algorithm called LT-Soundex, adapted for the Lithuanian language and enabling to index addresses components by phonetic similarity and perform approximate address searching. It is used with Levenshtein distance for effective approximate address searching.;">
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Malek, Z. Alksasbeh, A. Y. Alqaralleh Bassam, Abukhalil Tamer, Abukaraki Anas, Al Rawashdeh Tawfiq, and Al-Jaafreh Moha'med. "Smart detection of offensive words in social media using the soundex algorithm and permuterm index." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4431–38. https://doi.org/10.11591/ijece.v11i5.pp4431-4438.
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Offensive posts in the social media that are inappropriate for a specific age, level of maturity, or impression are quite often destined more to unadult than adult participants. Nowadays, the growth in the number of the masked offensive words in the social media is one of the ethically challenging problems. Thus, there has been growing interest in development of methods that can automatically detect posts with such words. This study aimed at developing a method that can detect the masked offensive words in which partial alteration of the word may trick the conventional monitoring systems when being posted on social media. The proposed method progresses in a series of phases that can be broken down into a pre-processing phase, which includes filtering, tokenization, and stemming; offensive word extraction phase, which relies on using the soundex algorithm and permuterm index; and a post-processing phase that classifies the users’ posts in order to highlight the offensive content. Accordingly, the method detects the masked offensive words in the written text, thus forbidding certain types of offensive words from being published. Results of evaluation of performance of the proposed method indicate a 99% accuracy of detection of offensive words.
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Alksasbeh, Malek Z., Bassam A. Y. Alqaralleh, Tamer Abukhalil, Anas Abukaraki, Tawfiq Al Rawashdeh, and Moha'med Al-Jaafreh. "Smart detection of offensive words in social media using the soundex algorithm and permuterm index." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4431. http://dx.doi.org/10.11591/ijece.v11i5.pp4431-4438.
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Offensive posts in the social media that are inappropriate for a specific age, level of maturity, or impression are quite often destined more to unadult than adult participants. Nowadays, the growth in the number of the masked offensive words in the social media is one of the ethically challenging problems. Thus, there has been growing interest in development of methods that can automatically detect posts with such words. This study aimed at developing a method that can detect the masked offensive words in which partial alteration of the word may trick the conventional monitoring systems when being posted on social media. The proposed method progresses in a series of phases that can be broken down into a pre-processing phase, which includes filtering, tokenization, and stemming; offensive word extraction phase, which relies on using the soundex algorithm and permuterm index; and a post-processing phase that classifies the users’ posts in order to highlight the offensive content. Accordingly, the method detects the masked offensive words in the written text, thus forbidding certain types of offensive words from being published. Results of evaluation of performance of the proposed method indicate a 99% accuracy of detection of offensive words.
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Cox, Shelley, Rohan Martin, Piyali Somaia, and Karen Smith. "The development of a data-matching algorithm to define the ‘case patient’." Australian Health Review 37, no. 1 (2013): 54. http://dx.doi.org/10.1071/ah11161.
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Objectives. To describe a model that matches electronic patient care records within a given case to one or more patients within that case. Method. This retrospective study included data from all metropolitan Ambulance Victoria electronic patient care records (n = 445 576) for the time period 1 January 2009–31 May 2010. Data were captured via VACIS (Ambulance Victoria, Melbourne, Vic., Australia), an in-field electronic data capture system linked to an integrated data warehouse database. The case patient algorithm included ‘Jaro–Winkler’, ‘Soundex’ and ‘weight matching’ conditions. Results. The case patient matching algorithm has a sensitivity of 99.98%, a specificity of 99.91% and an overall accuracy of 99.98%. Conclusions. The case patient algorithm provides Ambulance Victoria with a sophisticated, efficient and highly accurate method of matching patient records within a given case. This method has applicability to other emergency services where unique identifiers are case based rather than patient based. What is known about the topic? Accurate pre-hospital data that can be linked to patient outcomes is widely accepted as critical to support pre-hospital patient care and system performance. What does this paper add? There is a paucity of literature describing electronic matching of patient care records at the patient level rather than the case level. Ambulance Victoria has developed a complex yet efficient and highly accurate method for electronically matching patient records, in the absence of a patient-specific unique identifier. Linkage of patient information from multiple patient care records to determine if the records are for the same individual defines the ‘case patient’. What are the implications for practitioners? This paper describes a model of record linkage where patients are matched within a given case at the patient level as opposed to the case level. This methodology is applicable to other emergency services where unique identifiers are case based.
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Haris, Al Qodri Maarif, Surya Gunawan Teddy, and Akmeliawati Rini. "Adaptive language processing unit for Malaysian sign language synthesizer." IAES International Journal of Robotics and Automation (IJRA) 10, no. 4 (2021): 326–39. https://doi.org/10.11591/ijra.v10i4.pp326-339.
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Language processing unit (LPU) is a system built to process text-based data to comply with the rules of the sign language grammar. This system was developed as an important part of the sign language synthesizer system. Sign language (SL) uses different grammatical rules from the spoken/verbal language, which only involves the important words that hearing/impaired speech people can understand. Therefore, it needs word classification by LPU to determine grammatically processed sentences for the sign language synthesizer. However, the existing language processing unit in SL synthesizers suffers time lagging and complexity problems, resulting in high processing time. The two features, i.e., the computational time and success rate, become trade-offs which means the processing time becomes longer to achieve a higher success rate. This paper proposes an adaptive LPU that allows processing the words from spoken words to Malaysian SL grammatical rule that results in relatively fast processing time and a good success rate. It involves n-grams, natural language processing (NLP), and hidden Markov models (HMM)/Bayesian networks as the classifier to process the text-based input. As a result, the proposed LPU system has successfully provided an efficient (fast) processing time and a good success rate compared to LPU with other edit distances (mahalanobis, Levenshtein, and soundex). The system has been tested on 130 text-input sentences with several words ranging from 3 to 10 words. Results showed that the proposed LPU could achieve around 1.497ms processing time with an average success rate of 84.23% for a maximum of ten-word sentences.
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Ivanov, Serhii, Eugene Ivohin, and Mykhailo Makhno. "Automation of accounting of publications using the ORCID application programming interface." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 1 (2024): 141–46. http://dx.doi.org/10.17721/1812-5409.2024/1.26.
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The procedure for automated accounting of publications based on the use of Rest API of the ORCID database is proposed. The relevance of publication accounting is described. The importance of using various technologies for creating bibliographic data repositories is substantiated. The possibility of using API technology in the most famous publication databases such as Web of science, SCOPUS, Crossref, Google Scholar, and ORCID was analyzed. The possibility of using the ORCID database is substantiated. The scheme for downloading publications from the ORCID database by specified registration numbers based on services implemented in the Python and MatLab programming languages is given. The received data in JSON or XML is subject to further parsing. MatLab functions for obtaining a structure from XML (JSON) data formats are provided.In addition, the algorithm for finding duplicate publications during their accounting is considered. Approaches to avoid duplication of publications in databases based on the application of the Levenstein algorithm for similarity assessment are formulated. It is proposed to transliterate the Cyrillic alphabet into the Latin alphabet to ensure clarity and correct comparison of textual data. A MySql database was developed to collect and update data on publishing activity. The title of the publication table of the database is supplemented with a special attribute, which stores the results of the conversion of Cyrillic names into corresponding Latin names. It is recommended to use indexing of database table fields (INDEX) by various attributes, which allowed to significantly increase the efficiency of searching, processing and comparing data. It is proposed to use the Soundex() function as a MySQL DBMS tool to determine the level of consonance of publication topics by additional parameters. The practical implementation of the algorithm for finding duplicate publications and their numbering confirmed the constructiveness of the proposed approach which was confirmed when filling the database. This article is of interest to software developers.
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Michelle, Bao-Torayno, Jhoye M. Raboy Love, and S. Namoco Jr Consorcio. "A Text Clustering Preprocessing Technique for Mixed Bisaya and English Short Message Service (SMS) Messages for Higher Education Institutions (HEIs) Enrolment-Related Inquiries." Indian Journal of Science and Technology 13, no. 6 (2020): 654–73. https://doi.org/10.17485/ijst/2020/v13i06/149363.
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Abstract <strong>Objectives:</strong> This study is aimed to develop a text preprocessing technique for mixed Bisaya and English short message service (SMS) messages. This technique is used to extract significant keywords for SMS message clustering procedure as the basis for SMS automated response on Higher Education Institution (HEI)’s enrollmentrelated inquiries. <strong>Methods/statistical analysis:</strong> In this study, a text clustering preprocessing technique is introduced and developed for mixed Bisaya and English SMS messages for Higher Education Institution (HEI) enrollment-related inquiries. The technique is a relatively new approach to extract significant keywords while addressing key challenges in morphological complexities on mixed Bisaya and English SMS messages. The method has seven (7) stages namely: tokenization, language tagging, stop-word removal, stemming, Soundex, final-tagging, and language translation. The term frequency co-occurrence clustering approach is applied to evaluate the precision and effectiveness of the text preprocessing technique. <strong>Findings:</strong> Test results revealed that the method produces a good preprocessing procedure with approximately 73%–83% accuracy rate on text processing and 87%–90% accuracy rate when text preprocessing is applied to clustering. <strong>Application/ improvements:</strong> The results of this study may assist academic institutions in maximizing the opportunity to effectively entertain more enrollment-related inquiries via SMS as an alternative communication medium to its target market. This also promotes technological advancement for the institution as it utilizes an ICTenhanced marketing approach through mobile technology. <strong>Keywords:</strong> Text Preprocessing, Text Clustering, SMS Messaging, Stemming Algorithm, Enrollment-related Inquiries
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Parivash, Ranjbar. "Signal Processing Methods for Improvement of Environmental Perception of Persons with Deafblindness." Advanced Materials Research 902 (February 2014): 398–404. http://dx.doi.org/10.4028/www.scientific.net/amr.902.398.
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Environmental perception is a functional area that is severely limited in persons with deafblindness (DB) who belong a category of people with severe disabilities. Monitor is a vibratory aid developed with the aim to improve environmental perception of persons with DB. The aid consists of a mobile phone with an application connected to a microphone and vibrator. Monitor picks up the sounds produced by events by microphone, processes the sound using an algorithm programmed as an application in the mobile phone and then presents the signal via the vibrator to the persons with DB to be sensed and interpreted. In previous laboratory studies, four algorithms (AM, AMMC, TR, and TRHA) were developed based on modulating, and transposing principles. The algorithms were tested by persons with normal hearing/hearing impairment and selected as good candidates to improve vibratory identification of environmental sounds. In this on-going the algorithms are tested by 13 persons with congenital D and five persons with DB using Monitor in a realistic environment, living room, kitchen or office. Forty five recorded environmental sounds were used as test stimuli. The subjects tested the algorithms two times, Test and Retest each including a test session initiated by a training session. The four algorithms were tested in four days at Test and four days at Retest in total eight test days. Each test day began with a training session where a sound was presented as vibrations to be sensed by the person with the aim to remember its pattern and identity. The 45 sounds were grouped in four groups where an specific algorithm was chosen to process an specific sound group in a specific day. At the test session a sound was presented and the person was given 5 randomly chosen sound alternatives to choose the one as represented sound. The algorithms were different for different sound groups during four different test days so all algorithms were used to process all sounds. The algorithms were tested a second time, Retest, in same way as in Test. The mean value of identification of environmental sounds varied between 74.6% and 84.0% at Test and between 86.9% and 90.4% at Retest. The identification results at Retest were significantly improved (p<0.01) for all algorithms after a relatively short time of training indicating a good learning effect. At Test the algorithm AM was significantly better than the algorithms AMMC and TRHA (p< 0.01) and the algorithm TR was better than TRHA (p<0.01). The algorithms AM, AMMC, and TR were selected as good candidates to be implemented in the Monitor to improve environmental perception.
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Sathesh, K., S. Rajasekaran, P. Jayapal Reddy, T. Manasa, and G. Mythresh. "Real Time Lung Sound Separation from Cardiac Sounds by Adaptive Algorithm Technique." International Journal of Scientific Engineering and Research 10, no. 4 (2022): 1–4. https://doi.org/10.70729/se22324220809.
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Yue, Qing, Eric J. Fetzer, Likun Wang, et al. "Evaluating the consistency and continuity of pixel-scale cloud property data records from Aqua and SNPP (Suomi National Polar-orbiting Partnership)." Atmospheric Measurement Techniques 15, no. 7 (2022): 2099–123. http://dx.doi.org/10.5194/amt-15-2099-2022.
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Abstract. The Aqua, SNPP (Suomi National Polar-orbiting Partnership), and JPSS (Joint Polar Satellite System) satellites carry a combination of hyperspectral infrared sounders (AIRS, Atmospheric Infrared Sounder, and CrIS, Cross-track Infrared Sounder) and high-spatial-resolution narrowband imagers (MODIS, Moderate Resolution Imaging Spectroradiometer, and VIIRS, Visible Infrared Imaging Radiometer Suite). They provide an opportunity to acquire high-quality, long-term cloud data records and are a key component of the existing Program of Record of cloud observations. By matching observations from sounders and imagers across different platforms at the pixel scale, this study evaluates the self-consistency and continuity of cloud retrievals from Aqua and SNPP by multiple algorithms, including the AIRS version 7 retrieval algorithm and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) version 2 for sounders and the standard Aqua MODIS collection 6.1 and the NASA MODIS–VIIRS continuity cloud products for imagers. Metrics describing detailed statistical distributions at the sounder field of view (FOV) and the joint histograms of cloud properties are evaluated. These products are found to be highly consistent despite their retrieval from different sensors using different algorithms. Differences between the two sounder cloud products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric profile retrievals. The sounder subpixel cloud heterogeneity evaluated using the standard deviation of imager retrievals at the sounder FOV shows good agreement between the standard and continuity products from different satellites. However, the impact of algorithm and instrument differences between MODIS and VIIRS is revealed in cloud top pressure retrievals and in the imager cloud distribution skewness. Our study presents a unique aspect to examine NASA's progress toward building a continuous cloud data record with sufficient quality to investigate clouds' role in global environmental change.
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Yin, Yibo, Kainan Ma, and Ming Liu. "Temporal Convolutional Network Connected with an Anti-Arrhythmia Hidden Semi-Markov Model for Heart Sound Segmentation." Applied Sciences 10, no. 20 (2020): 7049. http://dx.doi.org/10.3390/app10207049.
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Heart sound segmentation (HSS) is a critical step in heart sound processing, where it improves the interpretability of heart sound disease classification algorithms. In this study, we aimed to develop a real-time algorithm for HSS by combining the temporal convolutional network (TCN) and the hidden semi-Markov model (HSMM), and improve the performance of HSMM for heart sounds with arrhythmias. We experimented with TCN and determined the best parameters based on spectral features, envelopes, and one-dimensional CNN. However, the TCN results could contradict the natural fixed order of S1-systolic-S2-diastolic of heart sound, and thereby the Viterbi algorithm based on HSMM was connected to correct the order errors. On this basis, we improved the performance of the Viterbi algorithm when detecting heart sounds with cardiac arrhythmias by changing the distribution and weights of the state duration probabilities. The public PhysioNet Computing in Cardiology Challenge 2016 data set was employed to evaluate the performance of the proposed algorithm. The proposed algorithm achieved an F1 score of 97.02%, and this result was comparable with the current state-of-the-art segmentation algorithms. In addition, the proposed enhanced Viterbi algorithm for HSMM corrected 30 out of 30 arrhythmia errors after checking one by one in the dataset.
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Xie, Liping, Wan Chen, Zhien Liu, and Chihua Lu. "Research on the Harmonics-Based the Optimization Algorithm for the Active Synthesis of Automobile Sound." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 266, no. 2 (2023): 728–34. http://dx.doi.org/10.3397/nc_2023_01_1045.
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The technology of active sound generation (ASG) for automobiles is one of the most effective methods to flexibly achieve the sound design that meets the expectations of different user groups, and the active sound synthesis algorithms are crucial for the implementation of ASG. In this paper, the Kaiser window function-based the harmonic synthesis algorithm of automobile sound is proposed to achieve the extraction of the order sounds of target automobile. And, the suitable fitting functions are utilized to construct the mathematical model between the engine speed information and the amplitude of the different order sound. Then, a random phase correction algorithm is proposed to ensure the coherence of the synthesized sounds. Finally, the analysis of simulation results verifies that the established method of the extraction and synthesis of order sound can meet the requirements of target sound quality. The algorithm proposed in this paper can be used for the development of real-time engineering of ASD technology for real vehicles.
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Murphy, Emma, Mathieu Lagrange, Gary Scavone, Philippe Depalle, and Catherine Guastavino. "Perceptual Evaluation of Rolling Sound Synthesis." Acta Acustica united with Acustica 97, no. 5 (2011): 840–51. http://dx.doi.org/10.3813/aaa.918464.
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Three listening tests were conducted to perceptually evaluate different versions of a new real-time synthesis approach for sounds of sustained contact interactions. This study aims to identify the most effective algorithm to create a realistic sound for rolling objects. In Experiment 1 and 2, participants were asked to rate the extent to which 6 different versions sounded like rolling sounds. Subsequently, in Experiment 3, participants compared the 6 versions best rated in Experiment 1 and 2, to the original recordings. Results are presented in terms of both statistical analysis of the most effective synthesis algorithm and qualitative user comments. On methodological grounds, the comparison of Experiments 1, 2 and 3 highlights major differences between judgments collected in reference to the original recordings as opposed to judgments based on memory representations of rolling sounds.
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Ngai, Chun Hau, Alexander J. Kilpatrick, and Aleksandra Ćwiek. "Sound symbolism in Japanese names: Machine learning approaches to gender classification." PLOS ONE 19, no. 3 (2024): e0297440. http://dx.doi.org/10.1371/journal.pone.0297440.
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This study investigates the sound symbolic expressions of gender in Japanese names with machine learning algorithms. The main goal of this study is to explore how gender is expressed in the phonemes that make up Japanese names and whether systematic sound-meaning mappings, observed in Indo-European languages, extend to Japanese. In addition to this, this study compares the performance of machine learning algorithms. Random Forest and XGBoost algorithms are trained using the sounds of names and the typical gender of the referents as the dependent variable. Each algorithm is cross-validated using k-fold cross-validation (28 folds) and tested on samples not included in the training cycle. Both algorithms are shown to be reasonably accurate at classifying names into gender categories; however, the XGBoost model performs significantly better than the Random Forest algorithm. Feature importance scores reveal that certain sounds carry gender information. Namely, the voiced bilabial nasal /m/ and voiceless velar consonant /k/ were associated with femininity, and the high front vowel /i/ were associated with masculinity. The association observed for /i/ and /k/ stand contrary to typical patterns found in other languages, suggesting that Japanese is unique in the sound symbolic expression of gender. This study highlights the importance of considering cultural and linguistic nuances in sound symbolism research and underscores the advantage of XGBoost in capturing complex relationships within the data for improved classification accuracy. These findings contribute to the understanding of sound symbolism and gender associations in language.
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Khota, Ahmed, Eric W. Cooper, and Yu Yan. "Synthesis of Non-Linguistic Utterances for Sound Design Support Using a Genetic Algorithm." Applied Sciences 14, no. 11 (2024): 4572. http://dx.doi.org/10.3390/app14114572.
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As social robots become more prevalent, they often employ non-speech sounds, in addition to other modes of communication, to communicate emotion and intention in an increasingly complex visual and audio environment. These non-speech sounds are usually tailor-made, and research into the generation of non-speech sounds that can convey emotions has been limited. To enable social robots to use a large amount of non-speech sounds in a natural and dynamic way, while expressing a wide range of emotions effectively, this work proposes an automatic method of sound generation using a genetic algorithm, coupled with a random forest model trained on representative non-speech sounds to validate each produced sound’s ability to express emotion. The sounds were tested in an experiment wherein subjects rated the perceived valence and arousal. Statistically significant clusters of sounds in the valence arousal space corresponded to different emotions, showing that the proposed method generates sounds that can readily be used in social robots.
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Zheng, Xinyu, Ruixi Tang, Jiang Wang, et al. "Research on Multi-Source Simultaneous Recognition Technology Based on Sagnac Fiber Optic Sound Sensing System." Photonics 10, no. 9 (2023): 1003. http://dx.doi.org/10.3390/photonics10091003.
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To solve the problem of multiple sound recognition in the application of Sagnac optical fiber acoustic sensing system, a multi-source synchronous recognition algorithm was proposed, which combined the VMD (variational modal decomposition) algorithm and MFCC (Mel-frequency cepstral coefficient algorithm) algorithm to pre-process the photoacoustic sensing signal, and uses BP neural network to recognize the photoacoustic sensing signal. The modal analysis and feature extraction theory of photoacoustic sensing signal based on the VMD and MFCC algorithms were presented. The signal recognition theory analysis and system recognition program design were completed based on the BP neural network. Signal acquisition of different sounds and verification experiments of the recognition system have been carried out in a laboratory environment based on the Sagnac fiber optic sound sensing system. The experimental results show that the proposed optical fiber acoustic sensing signal recognition algorithm has a simultaneous recognition rate better than 96.5% for six types of sounds, and the optical acoustic signal recognition takes less than 5.3 s, which has the capability of real-time sound detection and recognition, and provides the possibility of further application of the Sagnac-based optical fiber acoustic sensing system.
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Xu, Xingchen, Xingguang Geng, Zhixing Gao, Hao Yang, Zhiwei Dai, and Haiying Zhang. "Optimal Heart Sound Segmentation Algorithm Based on K-Mean Clustering and Wavelet Transform." Applied Sciences 13, no. 2 (2023): 1170. http://dx.doi.org/10.3390/app13021170.
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The accurate localization of S1 and S2 is essential for heart sound segmentation and classification. However, current direct heart sound segmentation algorithms have poor noise immunity and low accuracy. Therefore, this paper proposes a new optimal heart sound segmentation algorithm based on K-means clustering and Haar wavelet transform. The algorithm includes three parts. Firstly, this method uses the Viola integral method and Shannon’s energy-based algorithm to extract the function of the envelope of the heart sound energy. Secondly, the time–frequency domain features of the acquired envelope are extracted from different dimensions and the optimal peak is searched adaptively based on a dynamic segmentation threshold. Finally, K-means clustering and Haar wavelet transform are implemented to localize S1 and S2 of heart sounds in the time domain. After validation, the recognition rate of S1 reached 98.02% and that of S2 reached 96.76%. The model outperforms other effective methods that have been implemented. The algorithm has high robustness and noise immunity. Therefore, it can provide a new method for feature extraction and analysis of heart sound signals collected in clinical settings.
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Collins, Nick. "Experiments with a new customisable interactive evolution framework." Organised Sound 7, no. 3 (2002): 267–73. http://dx.doi.org/10.1017/s1355771802003060.
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This article collates results from a number of applications of interactive evolution as a sound designer's tool for exploring the parameter spaces of synthesis algorithms. Experiments consider reverberation algorithms, wavetable synthesis, synthesis of percussive sounds and an analytical solution of the stiff string. These projects share the property of being difficult to probe by trial and error sampling of the parameter space. Interactive evolution formed the guidance principle for what quickly proved a more effective search through the multitude of parameter settings.The research was supported by building an interactive genetic algorithm library in the audio programming language SuperCollider. This library provided reusable code for the user interfaces and the underlying genetic algorithm itself, whilst preserving enough generality to support the framework of each individual investigation.Whilst there is nothing new in the use of genetic algorithms in sound synthesis tasks, the experiments conducted here investigate new applications such as reverb design and an analytical stiff string model not previously encountered in the literature. Further, the focus of this work is now shifting more into algorithmic composition research, where the generative algorithms are less clear-cut than those of these experiments. Lessons learned from the deployment of interactive evolution in sound design problems are very useful as a reference for the extension of the problem set.
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Kanagasabai, Lenin. "Power loss reduction by gryllidae optimization algorithm." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 3 (2020): 179–84. https://doi.org/10.11591/ijict.v9i3.pp179-184.
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This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.
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McDonald, Andrew, Mark J. F. Gales, and Anurag Agarwal. "A recurrent neural network and parallel hidden Markov model algorithm to segment and detect heart murmurs in phonocardiograms." PLOS Digital Health 3, no. 11 (2024): e0000436. http://dx.doi.org/10.1371/journal.pdig.0000436.
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The detection of heart disease using a stethoscope requires significant skill and time, making it expensive and impractical for widespread screening in low-resource environments. Machine learning analysis of heart sound recordings can improve upon the accessibility and accuracy of diagnoses, but existing approaches require further validation on larger and more representative clinical datasets. For many previous algorithms, segmenting the signal into its individual sound components is a key first step. However, segmentation algorithms often struggle to find S1 or S2 sounds in the presence of strong murmurs or noise that significantly alter or mask the expected sound. Segmentation errors then propagate to the subsequent disease classifier steps. We propose a novel recurrent neural network and hidden semi-Markov model (HSMM) algorithm that can both segment the signal and detect a heart murmur, removing the need for a two-stage algorithm. This algorithm formed the ‘CUED_Acoustics’ entry to the 2022 George B. Moody PhysioNet challenge, where it won the first prize in both the challenge tasks. The algorithm’s performance exceeded that of many end-to-end deep learning approaches that struggled to generalise to new test data. As our approach both segments the heart sound and detects a murmur, it can provide interpretable predictions for a clinician. The model also estimates the signal quality of the recording, which may be useful for a screening environment where non-experts are using a stethoscope. These properties make the algorithm a promising tool for screening of abnormal heart murmurs.
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Lenin, Kanagasabai. "Power loss reduction by gryllidae optimization algorithm." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 3 (2020): 179. http://dx.doi.org/10.11591/ijict.v9i3.pp179-184.
Full textAbstract:
<p><span lang="EN-US">This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.</span></p>
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Park, Jihun. "Sound Propagation and Reconstruction Algorithm Based on Geometry." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 13 (2019): 86. http://dx.doi.org/10.3991/ijoe.v15i13.11212.
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This paper presents a method of simulating sound propagation and reconstruction for the virtual reality applications. The algorithm being developed in this paper is based on a ray sound theory. If we are given 3 dimensional geometry input as well as sound sources as inputs, we can compute sound effects over the entire boundary surfaces. In this paper, we present two approaches to compute sound field: The first approach, called forward tracing, traces sounds emanating from sound sources, while the second approach, called geometry based computation, computes possible propagation routes between sources and receivers. We compare two approaches and propose a geometry-based sound computation method for outdoor simulation. This approach is computationally more efficient than the forward sound tracing. The physical environment affects sound propagation simulation by impulse- response. When a sound source waveform and numerically computed impulse in time is convoluted, a synthetic sound is generated. This technique can be easily generalized to synthesize realistic stereo sounds for the virtual reality applications. At the same time, the simulation result can be visualized using VRML.
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Thoen, Bart, Stijn Wielandt, and Lieven De Strycker. "Improving AoA Localization Accuracy in Wireless Acoustic Sensor Networks with Angular Probability Density Functions." Sensors 19, no. 4 (2019): 900. http://dx.doi.org/10.3390/s19040900.
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Advances in energy efficient electronic components create new opportunities for wireless acoustic sensor networks. Such sensors can be deployed to localize unwanted and unexpected sound events in surveillance applications, home assisted living, etc. This research focused on a wireless acoustic sensor network with low-profile low-power linear MEMS microphone arrays, enabling the retrieval of angular information of sound events. The angular information was wirelessly transmitted to a central server, which estimated the location of the sound event. Common angle-of-arrival localization approaches use triangulation, however this article presents a way of using angular probability density functions combined with a matching algorithm to localize sound events. First, two computationally efficient delay-based angle-of-arrival calculation methods were investigated. The matching algorithm is described and compared to a common triangulation approach. The two localization algorithms were experimentally evaluated in a 4.25 m by 9.20 m room, localizing white noise and vocal sounds. The results demonstrate the superior accuracy of the proposed matching algorithm over a common triangulation approach. When localizing a white noise source, an accuracy improvement of up to 114% was achieved.
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Golpaygani, Ali Tavakoli, Nahid Abolpour, Kamran Hassani, Kourosh Bajelani, and D. John Doyle. "Detection and identification of S1 and S2 heart sounds using wavelet decomposition method." International Journal of Biomathematics 08, no. 06 (2015): 1550078. http://dx.doi.org/10.1142/s1793524515500783.
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Phonocardiogram (PCG), the digital recording of heart sounds is becoming increasingly popular as a primary detection system for diagnosing heart disorders and it is relatively inexpensive. Electrocardiogram (ECG) is used during the PCG in order to identify the systolic and diastolic parts manually. In this study a heart sound segmentation algorithm has been developed which separates the heart sound signal into these parts automatically. This study was carried out on 100 patients with normal and abnormal heart sounds. The algorithm uses discrete wavelet decomposition and reconstruction to produce PCG intensity envelopes and separates that into four parts: the first heart sound, the systolic period, the second heart sound and the diastolic period. The performance of the algorithm has been evaluated using 14,000 cardiac periods from 100 digital PCG recordings, including normal and abnormal heart sounds. In tests, the algorithm was over 93% correct in detecting the first and second heart sounds. The presented automatic segmentation algorithm using wavelet decomposition and reconstruction to select suitable frequency band for envelope calculations has been found to be effective to segment PCG signals into four parts without using an ECG.
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Jeon, Jin Yong, Na Young Kim, Sang Heon Kim, Hong Jin Kim, and Gwun Il Park. "Pneumonia diagnosis algorithm based on room impulse responses using cough sounds." Journal of the Acoustical Society of America 152, no. 4 (2022): A49—A50. http://dx.doi.org/10.1121/10.0015503.
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For data augmentation of the pneumonia diagnosis algorithm by deep learning, an image conversion method through a convolutional method utilizing spatial impulse and sound quality factors of cough sounds is proposed. First, reverberant spaces with different volumes were implemented, and spatial impulse responses were generated for each space through computer simulation of spatial models according to sound source and receiver points. Sound quality analysis was performed to improve accuracy, and 2-D sound quality data of time series was converted into 3-D image data using the Gramian Angular Field (GAF) method for combination between heterogeneous data. As a result, 97.5% accuracy was obtained for the configured dataset. The result of this study is expected to be used to develop diagnostic algorithms for various respiratory diseases including pneumonia in the future.
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Luque, Amalia, Javier Romero-Lemos, Alejandro Carrasco, and Julio Barbancho. "Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks." Sensors 18, no. 8 (2018): 2465. http://dx.doi.org/10.3390/s18082465.
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The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames, while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application.
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Kim, Eunjae, Sukwon Choi, Cheong Ghil Kim, and Woo-Chan Park. "Multi-Threaded Sound Propagation Algorithm to Improve Performance on Mobile Devices." Sensors 23, no. 2 (2023): 973. http://dx.doi.org/10.3390/s23020973.
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We propose a multi-threaded algorithm that can improve the performance of geometric acoustic (GA)-based sound propagation algorithms in mobile devices. In general, sound propagation algorithms require high computational cost because they perform based on ray tracing algorithms. For this reason, it is difficult to operate sound propagation algorithms in mobile environments. To solve this problem, we processed the early reflection and late reverberation steps in parallel and verified the performance in four scenes based on eight sound sources. The experimental results showed that the performance of the proposed method was on average 1.77 times better than that of the single-threaded method, demonstrating that our algorithm can improve the performance of mobile devices.
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Wei, Jennifer C., Laura L. Pan, Eric Maddy, et al. "Ozone Profile Retrieval from an Advanced Infrared Sounder: Experiments with Tropopause-Based Climatology and Optimal Estimation Approach." Journal of Atmospheric and Oceanic Technology 27, no. 7 (2010): 1123–39. http://dx.doi.org/10.1175/2010jtecha1384.1.
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Abstract Motivated by a significant potential for retrieving atmospheric ozone profile information from advanced satellite infrared sounders, this study investigates various methods to optimize ozone retrievals. A set of retrieval experiments has been performed to assess the impact of different background states (or the a priori states) and retrieval algorithms on the retrieved ozone profiles in the upper troposphere and lower stratosphere (UTLS) using Atmospheric Infrared Sounder (AIRS) measurements. A new tropopause-based ozone climatology, using publicly available global ozonesonde data to construct the a priori state, is described. Comparisons are made with the AIRS version 5 (v5) ozone climatology. The authors also present the result of a newly implemented optimal estimation (OE) algorithm and compare it to the current AIRS science team (AST) algorithm used in version 5. The ozone climatology using tropopause-referenced coordinates better preserves the shape and the magnitude of the ozone gradient across the tropopause, especially in the extratropical region. The results of the retrieval experiments indicate that the tropopause-referenced climatology not only helps to optimize the use of instrument sensitivity in the UTLS region, but it also provides better constraints to the OE algorithm.
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Xie, Liping, Chihua Lu, Zhien Liu, Yawei Zhu, and Weizhi Song. "A method of generating car sounds based on granular synthesis algorithm." Noise Control Engineering Journal 70, no. 4 (2022): 384–93. http://dx.doi.org/10.3397/1/377031.
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The active sound generation technique for automobiles, where car sounds can be synthesized by means of electronic sound production, is one of the effective methods to achieve the target sound. A method for active sound generation of automobile based on the granular synthesis algorithm is put forward here to avoid the broadband beating phenomena that occur due to the mismatch of parameters of sound granular signals. The comparison of the function expression of sound signal and Hilbert transformation is performed based on the principle of overlap and add; moreover, those parameters (phase, frequency and amplitude) of sound signals are interpolated by means of the Hermite interpolation algorithm which can ensure the continuity of the phase, frequency and amplitude curves. Thus, the transition audio is constructed by means of the sound signal function here to splice the adjacent sound granules. Our simulations show that our method can be applied to solve the current broadband beating issue for splicing sound granules and achieve natural continuity of synthesized car sounds. The subjective test results also indicate that our transition audio can produce high quality audio restitution.
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Andreev, Alexander A., and Marina S. Shapovalova. "SOUND RESEARCH. NOISE REMOVAL." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 1 (2022): 35–45. http://dx.doi.org/10.28995/2686-679x-2022-1-35-45.
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The article presents algorithms for removing extraneous noise from an audio track. It considers the features of various types of noise that occur during sound recording. The article takes into account the features of the Conv-TasNet architecture, which is based on the imposition of convolutions on a pure signal without frequency separation. There is an analysis of the DEMUC algorithm, which directly generates sources from the original signal, bypassing the intermediate prediction of masks; the architecture for segmentation of images U-Net is partially borrowed. Also the authors consider the HiFi-GA noise reduction algorithm, consisting of three main parts: Wavenet, Postnet and GAN. A clean signal based on a noisy one is created with the WaveNet algorithm that was originally used to translate text information into speech. A feature of different versions of the WaveNet algorithm for noise reduction is that a new signal can be generated both in its entirety and for each time-point. The paper also presents a mathematical apparatus for implementing the ConvTasNet, DEMUC, and HiFi-GA algorithms, analyzes in detail noise reduction when recording sound, explores various noise reduction methods, and formulates the advantages and disadvantages of each of them.
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Zhou, Jun, and Hu Yang. "Comparison of the Remapping Algorithms for the Advanced Technology Microwave Sounder (ATMS)." Remote Sensing 12, no. 4 (2020): 672. http://dx.doi.org/10.3390/rs12040672.
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One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the nonuniform spatial resolution. Remapping algorithms can be applied to the data to ameliorate this limitation. In this paper, two remapping algorithms, the Backus–Gilbert inversion (BGI) technique and the filter algorithm (AFA), widely used in the operational data preprocessing of the Advanced Technology Microwave Sounder (ATMS), are investigated. The algorithms are compared using simulations and actual ATMS data. Results show that both algorithms can effectively enhance or degrade the resolution of the data. The BGI has a higher remapping accuracy than the AFA. It outperforms the AFA by producing less bias around coastlines and hurricane centers where the signal changes sharply. It shows no obvious bias around the scan ends where the AFA has a noticeable positive bias in the resolution-enhanced image. However, the BGI achieves the resolution enhancement at the expense of increasing the noise by 0.5 K. The use of the antenna pattern instead of the point spread function in the algorithm causes the persistent bias found in the AFA-remapped image, leading not only to an inaccurate antenna temperature expression but also to the neglect of the geometric deformation of the along-scan field-of-views.
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Narváez, Pedro, and Winston S. Percybrooks. "Synthesis of Normal Heart Sounds Using Generative Adversarial Networks and Empirical Wavelet Transform." Applied Sciences 10, no. 19 (2020): 7003. http://dx.doi.org/10.3390/app10197003.
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Currently, there are many works in the literature focused on the analysis of heart sounds, specifically on the development of intelligent systems for the classification of normal and abnormal heart sounds. However, the available heart sound databases are not yet large enough to train generalized machine learning models. Therefore, there is interest in the development of algorithms capable of generating heart sounds that could augment current databases. In this article, we propose a model based on generative adversary networks (GANs) to generate normal synthetic heart sounds. Additionally, a denoising algorithm is implemented using the empirical wavelet transform (EWT), allowing a decrease in the number of epochs and the computational cost that the GAN model requires. A distortion metric (mel–cepstral distortion) was used to objectively assess the quality of synthetic heart sounds. The proposed method was favorably compared with a mathematical model that is based on the morphology of the phonocardiography (PCG) signal published as the state of the art. Additionally, different heart sound classification models proposed as state-of-the-art were also used to test the performance of such models when the GAN-generated synthetic signals were used as test dataset. In this experiment, good accuracy results were obtained with most of the implemented models, suggesting that the GAN-generated sounds correctly capture the characteristics of natural heart sounds.
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Stoll, Thomas. "Genomic: Combining Genetic Algorithms and Corpora to Evolve Sound Treatments." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 5 (2021): 50–54. http://dx.doi.org/10.1609/aiide.v10i5.12772.
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Genomic is Python software that evolves sound treatments and produce novel sounds. It offers features that have the potential to serve sound designers and composers, aiding them in their search for new and interesting sounds. This paper lays out the rationale and some design decisions made for Genomic, and proposes several intuitive ways of both using the software and thinking about the techniques that it enables for the modification and design of sound.
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Wiharto, Wiharto, Annas Abdurrahman, and Umi Salamah. "Detection of COVID-19 based on cough sound and accompanying symptom using LightGBM algorithm." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 940. https://doi.org/10.11591/ijeecs.v38.i2.pp940-949.
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Coronavirus disease 19 (COVID-19) is an infectious disease whose diagnosis is carried out using antigen-antibody tests and reverse transcription polymerase chain reaction (RT-PCR). Apart from these two methods, several alternative early detection methods using machine learning have been developed. However, it still has limitations in accessibility, is invasive, and its implementation involves many parties, which could potentially even increase the risk of spreading COVID-19. Therefore, this research aims to develop an alternative early detection method that is non-invasive by utilizing the LightGBM algorithm to detect COVID-19 based on the results of feature extraction from cough sounds and accompanying symptoms that can be identified independently. This research uses cough sound samples and symptom data from the Coswara dataset, and cough sound’s features were extracted using the log mel-spectrogram, mel frequency cepstrum coefficient (MFCC), chroma, zero crossing rate (ZCR), and root mean square (RMS) methods. Next, the cough sound features are combined with symptom data to train the LightGBM. The model trained using cough sound features and patient symptoms obtained the best performance with 95.61% accuracy, 93.33% area under curve (AUC), 88.74% sensitivity, 97.91% specificity, 93.17% positive prediction value (PPV), and 96.33% negative prediction value (NPV). It can be concluded that the trained model has excellent classification capabilities based on the AUC values obtained.
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Baakek, Yettou Nour El Houda, Imane Debbal, Hidayat Boudis, and Sidi Mohammed El Amine Debbal. "Study of the impact of clicks and murmurs on cardiac sounds S1 and S2 through bispectral analysis." Polish Journal of Medical Physics and Engineering 27, no. 1 (2021): 63–72. http://dx.doi.org/10.2478/pjmpe-2021-0009.
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Abstract This paper presents a study of the impact of clicks, and murmurs on cardiac sound S1, and S2, and the measure of severity degree through synchronization degree between frequencies, using bispectral analysis. The algorithm is applied on three groups of Phonocardiogram (PCG) signal: group A represents PCG signals having a morphology similar to that of the normal PCG signal without click or murmur, group B represents PCG signals with a click (reduced murmur), and group C represent PCG signals with murmurs. The proposed algorithm permits us to evaluate and quantify the relationship between the two sounds S1 and S2 on one hand and between the two sounds, click and murmur on the other hand. The obtained results show that the clicks and murmurs can affect both the heart sounds, and vice versa. This study shows that the heart works in perfect harmony and that the frequencies of sounds S1, S2, clicks, and murmurs are not accidentally generated; but they are generated by the same generator system. It might also suggest that one of the obtained frequencies causes the others. The proposed algorithm permits us also to determine the synchronization degree. It shows high values in group C; indicating high severity degrees, low values for group B, and zero in group A. The algorithm is compared to Short-Time Fourier Transform (STFT) and continuous wavelet transform (CWT) analysis. Although the STFT can provide correctly the time, it can’t distinguish between the internal components of sounds S1 and S2, which are successfully determined by CWT, which, in turn, cannot find the relationship between them. The algorithm was also evaluated and compared to the energetic ratio. the obtained results show very satisfactory results and very good discrimination between the three groups. We can conclude that the three algorithms (STFT, CWT, and bispectral analysis) are complementary to facilitate a good approach and to better understand the cardiac sounds
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Nieblas, Carlos I. "Parallel Matching Pursuit Algorithm to Decomposition of Heart Sound Using Gabor Dictionaries." International Journal of Signal Processing Systems 9, no. 4 (2021): 22–28. http://dx.doi.org/10.18178/ijsps.9.4.22-28.
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In order to improve the performance of matching pursuit algorithm, we propose a Parallel Matching Pursuit algorithm to decompose phonocardiogram sounds of long duration. The main goal is to demonstrate the performance of the Parallel Matching Pursuit algorithm (PMP) compared with traditional iterative matching pursuit algorithm to decompose normal and pathological heart sounds of Phonocardiogram (PCG) using a Gabor dictionary. This architecture is implemented in open source Java SE 8 using a concurrency library, which is able to reduce computational cost using multi-threading until 83 % compared with traditional Matching Pursuit. Java language is widely used in the creation of web components and enterprise solutions so based on this point the main idea of this research is to set the base to implement Parallel Matching Pursuit algorithm (PMP) on web platforms focused on the monitoring of heart to sounds. This implementation allows exploring and applying iterative algorithms or sparse approximation which require processing long audio signals with low processing time.
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Muhammad Naqiuddin Zaini, Marina Yusoff, and Muhammad Amirul Sadikin. "Forest Sound Event Detection with Convolutional Recurrent Neural Network-Long Short-Term Memory." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 2 (2023): 242–54. http://dx.doi.org/10.37934/araset.32.2.242254.
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Sound event detection tackles an audio environment's complex sound analysis and recognition problem. The process involves localizing and classifying sounds mainly to estimate the start point and end points of the separate sounds and describe each sound. Sound event detection capability relies on the type of sound. Although detecting sequences of distinct temporal sounds is straightforward, the situation becomes complex when the sound is multiple overlapping of much single audio. This situation usually occurs in the forest environment. Therefore, this aim of the paper is to propose a Convolution Recurrent Neural Network-Long Short-Term Memory algorithm to detect an audio signature of intruders in the forest environment. The audio is extracted in the Mel-frequency cepstrum coefficient and fed into the algorithm as an input. Six sound categories are chainsaw, machete, car, hatchet, ambiance, and bike. They were tested using several epochs, batch size, and filter of the layer in the model. The proposed model can achieve an accuracy of 98.52 percent in detecting the audio signature with a suitable parameter selection. In the future, additional types of audio signatures of intruders and further aspects of evaluation can be added to make the algorithm better at detecting intruders in the forest environment.
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Martinek, Jozef, P. Klco, M. Vrabec, T. Zatko, M. Tatar, and M. Javorka. "Cough Sound Analysis." Acta Medica Martiniana 13, Supplement-1 (2013): 15–20. http://dx.doi.org/10.2478/acm-2013-0002.
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Abstract Cough is the most common symptom of many respiratory diseases. Currently, no standardized methods exist for objective monitoring of cough, which could be commercially available and clinically acceptable. Our aim is to develop an algorithm which will be capable, according to the sound events analysis, to perform objective ambulatory and automated monitoring of frequency of cough. Because speech is the most common sound in 24-hour recordings, the first step for developing this algorithm is to distinguish between cough sound and speech. For this purpose we obtained recordings from 20 healthy volunteers. All subjects performed continuous reading of the text from the book with voluntary coughs at the indicated instants. The obtained sounds were analyzed using by linear and non-linear analysis in the time and frequency domain. We used the classification tree for the distinction between cough sound and speech. The median sensitivity was 100% and the median specificity was 95%. In the next step we enlarged the analyzed sound events. Apart from cough sounds and speech the analyzed sounds were induced sneezing, voluntary throat and nasopharynx clearing, voluntary forced ventilation, laughing, voluntary snoring, eructation, nasal blowing and loud swallowing. The sound events were obtained from 32 healthy volunteers and for their analysis and classification we used the same algorithm as in previous study. The median sensitivity was 86% and median specificity was 91%. In the final step, we tested the effectiveness of our developed algorithm for distinction between cough and non-cough sounds produced during normal daily activities in patients suffering from respiratory diseases. Our study group consisted from 9 patients suffering from respiratory diseases. The recording time was 5 hours. The number of coughs counted by our algorithm was compared with manual cough counts done by two skilled co-workers. We have found that the number of cough analyzed by our algorithm and manual counting, as well, were disproportionately different. For that reason we have used another methods for the distinction of cough sound from non-cough sounds. We have compared the classification tree and artificial neural networks. Median sensitivity was increasing from 28% (classification tree) to 82% (artificial neural network), while the median specificity was not changed significantly. We have enlarged our characteristic parameters of the Mel frequency cepstral coefficients, the weighted Euclidean distance and the first and second derivative in time. Likewise the modification of classification algorithm is under our interest
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Kim, Hyun-Don, Kazunori Komatani, Tetsuya Ogata, and Hiroshi G. Okuno. "Binaural Active Audition for Humanoid Robots to Localise Speech over Entire Azimuth Range." Applied Bionics and Biomechanics 6, no. 3-4 (2009): 355–67. http://dx.doi.org/10.1155/2009/817874.
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We applied motion theory to robot audition to improve the inadequate performance. Motions are critical for overcoming the ambiguity and sparseness of information obtained by two microphones. To realise this, we first designed a sound source localisation system integrated with cross-power spectrum phase (CSP) analysis and an EM algorithm. The CSP of sound signals obtained with only two microphones was used to localise the sound source without having to measure impulse response data. The expectation-maximisation (EM) algorithm helped the system to cope with several moving sound sources and reduce localisation errors. We then proposed a way of constructing a database for moving sounds to evaluate binaural sound source localisation. We evaluated our sound localisation method using artificial moving sounds and confirmed that it could effectively localise moving sounds slower than 1.125 rad/s. Consequently, we solved the problem of distinguishing whether sounds were coming from the front or rear by rotating and/or tipping the robot's head that was equipped with only two microphones. Our system was applied to a humanoid robot called SIG2, and we confirmed its ability to localise sounds over the entire azimuth range as the success rates for sound localisation in the front and rear areas were 97.6% and 75.6% respectively.
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Chi, Wayne, Steve Chien, and Jagriti Agrawal. "Scheduling with Complex Consumptive Resources for a Planetary Rover." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 348–56. http://dx.doi.org/10.1609/icaps.v30i1.6680.
Full textAbstract:
Generating and scheduling activities is particularly challenging when considering both consumptive resources and complex resource interactions such as time-dependent resource usage. We present three methods of determining valid temporal placement intervals for an activity in a temporally grounded plan in the presence of such constraints. We introduce the Max Duration and Probe algorithms which are sound, but incomplete, and the Linear algorithm which is sound and complete for linear rate resource consumption. We apply these techniques to the problem of scheduling awake and asleep episodes for a planetary rover where the awake durations are affected by scheduled activities. We demonstrate how the Probe algorithm performs competitively with the Linear algorithm given an advantageous problem space and well-defined heuristics. We show that the Probe and Linear algorithms outperform the Max Duration algorithm empirically. We then present the runtime differences between the three algorithms. The Probe algorithm is currently base-lined for use in the onboard scheduler for NASA's next planetary rover, the Mars 2020 rover.
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Chung, Youngbeen, Jie Jin, Hyun In Jo, et al. "Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI." Sensors 21, no. 21 (2021): 7036. http://dx.doi.org/10.3390/s21217036.
Full textAbstract:
Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.
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Küçükakarsu, Mustafa, Ahmet Reşit Kavsaoğlu, Fayadh Alenezi, Adi Alhudhaif, Raghad Alwadie, and Kemal Polat. "A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals." Diagnostics 13, no. 3 (2023): 575. http://dx.doi.org/10.3390/diagnostics13030575.
Full textAbstract:
This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.
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Ewers, Megan B., Adina Edwards, and Martin Siderius. "Comparison of conventional and adaptive acoustic beamforming algorithms using a tetrahedral microphone array in noisy environments." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A171—A172. http://dx.doi.org/10.1121/10.0027207.
Full textAbstract:
In situ acoustic measurements are often plagued by interfering sound sources that occur within the measurement environment. Both adaptive and conventional beamforming algorithms, when applied to the outputs of a microphone array arranged in a tetrahedral geometry, are able to capture sound sources in desired directions and reject sound from unwanted directions. Adaptive algorithms may be able to measure a desired sound source with greater spatial precision, but require more calculations and, therefore, computational power. A conventional frequency-domain phase-shift algorithm and a modified adaptive frequency-domain Minimum Variance Distortionless Response (MVDR) algorithm were applied to simulated and recorded signals from a tetrahedral array of omnidirectional microphones. The algorithms are described mathematically and demonstrated on both deterministic and real-world sound data, to quantitatively validate and compare their performance and to provide listening examples of their outputs in a variety of acoustically replicated environments. [Work supported by Portland State University.]
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Souidi, Abdelhakim, Sidi Mohammed El Amine Debbal, and Fadia Meziani. "The study of the impact of murmurs on heart sounds by using multiple signal classification pseudo-spectrum." Polish Journal of Medical Physics and Engineering 30, no. 2 (2024): 99–105. http://dx.doi.org/10.2478/pjmpe-2024-0011.
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Abstract The aim of this study was to present a novel framework for the analysis of the impact of murmurs on heart sounds recorded in real clinical environment. Heart sound records were rigorously selected from the PASCAL dataset using an automated signal quality assessment algorithm. Recordings from 159 patients were analyzed for spectral differences in normal, systolic and diastolic murmurs using a Multiple signal classification algorithm pseudo-spectrum. The spectral features evaluated for the first heart sound (S1) and the second heart sound (S2) were: energy, frequency and frequency density. Results show increased energy of fundamental heart sounds in systolic and diastolic murmurs similarly, whilst frequency is decreased inversely. Furthermore, the frequency density of the first and second heart sounds decreases in murmurs and it is shown to be the lowest in systolic murmur cases.
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Gebrekidan, Semere B., and Steffen Marburg. "Deep reinforcement learning for optimal sound absorbing structures design." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A232. http://dx.doi.org/10.1121/10.0023377.
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Deep learning algorithms have demonstrated a tremendous success in designing structures that surpass human capabilities. Based on the recent achievements of deep reinforcement learning in surpassing human capabilities, this paper focuses on implementing these algorithms to design optimal configurations of solid and porous materials that achieve a broadband absorption within the frequency range of 300 Hz to 3000 Hz. We employ model-free approaches, specifically deep Q-learning, double deep Q-learning, and dueling deep Q-learning algorithms, to predict material configurations that optimize absorption without requiring expertise knowledge. From a 230×30 different material combinations, the deep reinforcement algorithms learn to predict configurations that yield optimal absorption in few hundred steps. We discuss further the superior performance of a dueling deep learning algorithm compared to the other two deep learning approaches and a heuristic approach, such as genetic algorithm. The proposed model-free algorithms enable the prediction of absorption performance for any material configurations without the need for expertise.