Journal articles: 'LPC coefficients'

1

Choi, Jae-Seung. "Speaker Recognition using LPC cepstrum Coefficients and Neural Network." Journal of the Korean Institute of Information and Communication Engineering 15, no. 12 (December 31, 2011): 2521–26. http://dx.doi.org/10.6109/jkiice.2011.15.12.2521.

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Olive, Joseph P. "Mixed spectral representation—Formants and LPC coefficients." Journal of the Acoustical Society of America 85, S1 (May 1989): S59. http://dx.doi.org/10.1121/1.2027054.

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Jung, Won-Jin, and Moo-Young Kim. "Quantization of LPC Coefficients Using a Multi-frame AR-model." Journal of the Acoustical Society of Korea 31, no. 2 (February 29, 2012): 93–99. http://dx.doi.org/10.7776/ask.2012.31.2.093.

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Pérez, María Salomé, and Enrique Carrera. "LPC-based Feature Coefficients for Voice Authentication Tasks." MASKAY 2, no. 1 (November 1, 2012): 73. http://dx.doi.org/10.24133/maskay.v2i1.151.

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Voice authentication is a promising biometric technique based on extracting important information from the speech signal by means of computing a vector of feature coefficients. Based on that, this paper evaluates the effectiveness of linear predictive coefficients when combined with other simple metrics in voice authentication tasks. Linear predictive coefficients were chosen due to their relatively good performance and their not-so-complicated structures when compared to other similar alternatives. All the feature coefficients have been evaluated through an extensive parameter space study in order to apprehend the main limitations and potentials of voice authentication under different scenarios. For such an evaluation, a classifier based on artificial neural networks has been implemented.

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Hong Kook Kim, Seung Ho Choi, and Hwang Soo Lee. "On approximating line spectral frequencies to LPC cepstral coefficients." IEEE Transactions on Speech and Audio Processing 8, no. 2 (March 2000): 195–99. http://dx.doi.org/10.1109/89.824705.

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Sanches, I. "From LPC to normalised autocorrelation coefficients through a matrix." Electronics Letters 34, no. 4 (1998): 333. http://dx.doi.org/10.1049/el:19980310.

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Mohd Ali, Yusnita, Alhan Farhanah Abd Rahim, Emilia Noorsal, Zuhaila Mat Yassin, Nor Fadzilah Mokhtar, and Mohamad Helmy Ramlan. "Fuzzy-based voiced-unvoiced segmentation for emotion recognition using spectral feature fusions." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (July 1, 2020): 196. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp196-206.

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Despite abundant growth in automatic emotion recognition system (ERS) studies using various techniques in feature extractions and classifiers, scarce sources found to improve the system via pre-processing techniques. This paper proposed a smart pre-processing stage using fuzzy logic inference system (FIS) based on Mamdani engine and simple time-based features i.e. zero-crossing rate (ZCR) and short-time energy (STE) to initially identify a frame as voiced (V) or unvoiced (UV). Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) were tested with K-nearest neighbours (KNN) classifiers to evaluate the proposed FIS V-UV segmentation. We also introduced two feature fusions of MFCC and LPC with formants to obtain better performance. Experimental results of the proposed system surpassed the conventional ERS which yielded a rise in accuracy rate from 3.7% to 9.0%. The fusion of LPC and formants named as SFF LPC-fmnt indicated a promising result between 1.3% and 5.1% higher accuracy rate than its baseline features in classifying between neutral, angry, happy and sad emotions. The best accuracy rates yielded for male and female speakers were 79.1% and 79.9% respectively using SFF MFCC-fmnt fusion technique.

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Singh, Mandeep, and Gurpreet Singh. "Word recognition from speech signal using linear predictive coding and spectrum analysis." International Journal of Engineering & Technology 7, no. 3 (July 16, 2018): 1531. http://dx.doi.org/10.14419/ijet.v7i3.13285.

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This paper presents a technique for isolated word recognition from speech signal using Spectrum Analysis and Linear Predictive Coding (LPC). In the present study, only those words have been analyzed which are commonly used during a telephonic conversations by criminals. Since each word is characterized by unique frequency spectrum signature, thus, spectrum analysis of a speech signal has been done using certain statistical parameters. These parameters help in recognizing a particular word from a speech signal, as there is a unique value of a feature for each word, which helps in distinguishing one word from the other. Second method used is based on LPC coefficients. Analysis of features extracted using LPC coefficients help in identification of a specific word from the input speech signal. Finally, a combination of best features from these two methods has been used and a hybrid technique is proposed. An accuracy of 94% has been achieved for sample size of 400 speech words.

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., PPS Subhashini. "TEXT-INDEPENDENT SPEAKER RECOGNITION USING COMBINED LPC AND MFC COEFFICIENTS." International Journal of Research in Engineering and Technology 03, no. 06 (June 25, 2014): 508–14. http://dx.doi.org/10.15623/ijret.2014.0306095.

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Moriya, Takehiro. "Method for the modification of LPC coefficients of acoustic signals." Journal of the Acoustical Society of America 104, no. 5 (November 1998): 2554. http://dx.doi.org/10.1121/1.423836.

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Abdul, Zrar Khalid. "Kurdish Spoken Letter Recognition based on k-NN and SVM Model." Journal of University of Raparin 7, no. 4 (November 30, 2020): 1–12. http://dx.doi.org/10.26750/vol(7).no(4).paper1.

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Automatic recognition of spoken letters is one of the most challenging tasks in the area of speech recognition system. In this paper, different machine learning approaches are used to classify the Kurdish alphabets such as SVM and k-NN where both approaches are fed by two different features, Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCCs). Moreover, the features are combined together to learn the classifiers. The experiments are evaluated on the dataset that are collected by the authors as there as not standard Kurdish dataset. The dataset consists of 2720 samples as a total. The results show that the MFCC features outperforms the LPC features as the MFCCs have more relative information of vocal track. Furthermore, fusion of the features (MFCC and LPC) is not capable to improve the classification rate significantly.

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Yang, Jie. "Combining Speech Enhancement and Cepstral Mean Normalization for LPC Cepstral Coefficients." Key Engineering Materials 474-476 (April 2011): 349–54. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.349.

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A mismatch between the training and testing in noisy circumstance often causes a drastic decrease in the performance of speech recognition system. The robust feature coefficients might suppress this sensitivity of mismatch during the recognition stage. In this paper, we investigate the noise robustness of LPC Cepstral Coefficients (LPCC) by using speech enhancement with feature post-processing. At front-end, speech enhancement in the wavelet domain is used to remove noise components from noisy signals. This enhanced processing adopts the combination of discrete wavelet transform (DWT), wavelet packet decomposition (WPD), multi-thresholds processing etc to obtain the estimated speech. The feature post-processing employs cepstral mean normalization (CMN) to compensate the signal distortion and residual noise of enhanced signals in the cepstral domain. The performance of digit speech recognition systems is evaluated under noisy environments based on NOISEX-92 database. The experimental results show that the presented method exhibits performance improvements in the adverse noise environment compared with the previous features.

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Sani, Dian Ahkam, and Muchammad Saifulloh. "Speech to Text Processing for Interactive Agent of Virtual Tour Navigation." International Journal of Artificial Intelligence & Robotics (IJAIR) 1, no. 1 (October 31, 2019): 31. http://dx.doi.org/10.25139/ijair.v1i1.2030.

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The development of science and technology is one way to replace the method of human interaction with computers, one of which is to provide voice input. Conversion of sound into text form with the Backpropagation method can be understood and realized through feature extraction, including the use of Linear Predictive Coding (LPC). Linear Predictive Coding is one way to represent the signal in obtaining the features of each sound pattern. In brief, the way this speech recognition system worked was by inputting human voice through a microphone (analog signal) which then sampled with a sampling speed of 8000 Hz so that it became a digital signal with the assistance of sound card on the computer. The digital signal from the sample then entered the initial process using LPC, so that several LPC coefficients were obtained. The LPC outputs were then trained using the Backpropagation learning method. The results of the learning were classified with a word and stored in a database afterwards. The results of the test were in the form of an introduction program that able display the voice plots. the results of speech recognition with voice recognition percentage of respondents in the database iss 80% of the 100 data in the test in Real Time

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Ireton, Mark A. "System and method for performing predictive scaling in computing LPC speech coding coefficients." Journal of the Acoustical Society of America 103, no. 5 (1998): 2262. http://dx.doi.org/10.1121/1.422722.

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Mehling, Tanja, Thomas Ingram, Sandra Storm, Ulrich Bobe, Fang Liu, Martin Michel, and Irina Smirnova. "Estimation of LPC/water partition coefficients using molecular modeling and micellar liquid chromatography." Colloids and Surfaces A: Physicochemical and Engineering Aspects 431 (August 2013): 105–13. http://dx.doi.org/10.1016/j.colsurfa.2013.04.028.

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Mongia, Puneet Kumar, and R. K. Sharma. "Estimation and Statistical Analysis of Human Voice Parameters to Investigate the Influence of Psychological Stress and to Determine the Vocal Tract Transfer Function of an Individual." Journal of Computer Networks and Communications 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/290147.

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In this study the principal focus is to examine the influence of psychological stress (both positive and negative stress) on the human articulation and to determine the vocal tract transfer function of an individual using inverse filtering technique. Both of these analyses are carried out by estimating various voice parameters. The outcomes of the analysis of psychological stress indicate that all the voice parameters are affected due to the influence of stress on humans. About 35 out of 51 parameters follow a unique course of variation from normal to positive and negative stress in 32% of the total analyzed signals. The upshot of the analysis is to determine the vocal tract transfer function for each vowel for an individual. The analysis indicates that it can be computed by estimating the mean of the pole zero plots of that individual’s vocal tract estimated for the whole day. Besides this, an analysis is presented to find the relationship between the LPC coefficients of the vocal tract and the vocal tract cavities. The results of the analysis indicate that all the LPC coefficients of the vocal tract are affected due to change in the position of any cavity.

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Helmiyah, Siti, Imam Riadi, Rusydi Umar, and Abdullah Hanif. "Ekstraksi Fitur Pengenalan Emosi Berdasarkan Ucapan Menggunakan Linear Predictor Ceptral Coeffecient Dan Mel Frequency Cepstrum Coefficients." Mobile and Forensics 1, no. 2 (December 24, 2019): 48. http://dx.doi.org/10.12928/mf.v1i2.1259.

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Ucapan suara memiliki informasi penting yang dapat diterima oleh otak melalui gelombang suara. Otak menerima gelombang suara melalui alat pendengaran dan menghasilkan suatu informasi berupa pesan, bahasa, dan emosi. Pengenalan emosi wicara merupakan teknologi yang dirancang untuk mengidentifikasi keadaan emosi seseorang dari sinyal ucapannya. Hal tersebut menarik untuk diteliti, karena berkaitan dengan teknologi zaman sekarang yaitu pada penggunaan smartphone di berbagai macam aktivitas sehari-hari. Penelitian ini membandingkan ekstraksi fitur Metode LPC dan Metode MFCC. Kedua metode ekstraksi tersebut diklasifikasi menggunakan Metode Jaringan Syaraf Tiruan (MLP) untuk pengenalan emosi. Masing-masing metode menggunakan data emosi marah, bosan, bahagia, netral, dan sedih. Data dibagi menjadi dua, yaitu data testing dan data data training dengan perbandingan 80:20. Arsitektur jaringan yang digunakan adalah tiga lapisan yaitu lapisan input, lapisan tersembunyi, dan lapisan output. Parameter MLP yang digunakan learning rate = 0.0001, epsilon = 1e-08, epoch = 500, dan Cross Validation = 5. Hasil akurasi pengenalan emosi dengan ekstraksi fitur LPC sebesar adalah 28%. Sedangkan hasil akurasi dengan ekstraksi fitur MFCC sebesar 61,33%. Hasil akurasi ini bisa ditingkatkan dengan menambahkan data yang lebih banyak lagi, terutama untuk data testing. Perlunya pengujian pada nilai parameter jaringan MLP, yaitu dengan mengubah nilai-nilai parameter, karena dapat mempengaruhi tingkat akurasi pengenalan. Selain itu penentuan ekstraksi fitur dan klasifikasi metode yang lain juga dapat digunakan untuk mencari nilai akurasi pengenalan emosi yang lebih baik lagi.

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CHIKH, M. A., and F. BEREKSI–REGUIG. "CLASSIFICATION OF VENTRICULAR ECTOPIC BEATS (VEB'S) USING NEURAL NETWORKS." Journal of Mechanics in Medicine and Biology 04, no. 03 (September 2004): 333–40. http://dx.doi.org/10.1142/s0219519404001089.

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The most widely used signal in clinical practice is the electrocardiogram (ECG). ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. Thus, the required tasks of ECG processing are the reliable recognition of these waves, and the accurate measurement of clinically important parameters measured from the temporal distribution of the ECG constituent waves. The purpose of this paper is the classification of ventricular ectopic beats (VEB's). This research includes noise handling, feature extraction, and neural classification, all integrated in a three-stage procedure. Thirty features extracted from the morphology of the QRS segment, are reduced to seven coefficients using principal component analysis (PCA) and two coefficients using linear predictive coding (LPC) technique in addition to two other temporal parameters were used separately as the input of two neural network classifiers. The neural classifiers were tested on the MIT-BIH database and high scores were obtained for both sensitivity and specificity (84.88% and 91.92% respectively using ACP technique, and 76.17% and 88.95% using LPC method). This study confirms the power of artificial neural networks in the classification of normal and abnormal VEB beats. Clinical use of this method, however, still requires further investigation.

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Al Mahmud, Nahyan, and Shahfida Amjad Munni. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International journal of Multimedia & Its Applications 12, no. 5 (October 30, 2020): 1–8. http://dx.doi.org/10.5121/ijma.2020.12501.

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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) have also been used. These two methods closely resemble the human auditory system. These feature vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to investigate the nature of those acoustic features.

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Aggarwal, Gaurav, and Latika Singh. "Comparisons of Speech Parameterisation Techniques for Classification of Intellectual Disability Using Machine Learning." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 16–34. http://dx.doi.org/10.4018/ijcini.2020040102.

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Classification of intellectually disabled children through manual assessment of speech at an early age is inconsistent, subjective, time-consuming and prone to error. This study attempts to classify the children with intellectual disabilities using two speech feature extraction techniques: Linear Predictive Coding (LPC) based cepstral parameters, and Mel-frequency cepstral coefficients (MFCC). Four different classification models: k-nearest neighbour (k-NN), support vector machine (SVM), linear discriminant analysis (LDA) and radial basis function neural network (RBFNN) are employed for classification purposes. 48 speech samples of each group are taken for analysis, from subjects with a similar age and socio-economic background. The effect of the different frame length with the number of filterbanks in the MFCC and different frame length with the order in the LPC is also examined for better accuracy. The experimental outcomes show that the projected technique can be used to help speech pathologists in estimating intellectual disability at early ages.

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Trabelsi, Imen, and Med Salim Bouhlel. "Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition." International Journal of Synthetic Emotions 7, no. 1 (January 2016): 58–68. http://dx.doi.org/10.4018/ijse.2016010105.

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Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

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Girin, L. "Joint Matrix Quantization of Face Parameters and LPC Coefficients for Low Bit Rate Audiovisual Speech Coding." IEEE Transactions on Speech and Audio Processing 12, no. 3 (May 2004): 265–76. http://dx.doi.org/10.1109/tsa.2003.822626.

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Niyada, Katsuyuki, and Masakatsu Hoshimi. "Consonant recognition methods for unspecified speakers using bpf powers and time sequence of LPC cepstrum coefficients." Systems and Computers in Japan 18, no. 6 (1987): 47–59. http://dx.doi.org/10.1002/scj.4690180605.

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Yagimli, Mustafa, and Huseyin Kursat Tezer. "Navigation Security Module with Real-Time Voice Command Recognition System." Polish Maritime Research 24, no. 2 (June 27, 2017): 17–26. http://dx.doi.org/10.1515/pomr-2017-0046.

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Abstract The real-time voice command recognition system used for this study, aims to increase the situational awareness, therefore the safety of navigation, related especially to the close manoeuvres of warships, and the courses of commercial vessels in narrow waters. The developed system, the safety of navigation that has become especially important in precision manoeuvres, has become controllable with voice command recognition-based software. The system was observed to work with 90.6% accuracy using Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW) parameters and with 85.5% accuracy using Linear Predictive Coding (LPC) and DTW parameters.

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Jacob, Agnes, and P. Mythili. "Developing a Child Friendly Text-to-Speech System." Advances in Human-Computer Interaction 2008 (2008): 1–6. http://dx.doi.org/10.1155/2008/597971.

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This paper discusses the implementation details of a child friendly, good quality, English text-to-speech (TTS) system that is phoneme-based, concatenative, easy to set up and use with little memory. Direct waveform concatenation and linear prediction coding (LPC) are used. Most existing TTS systems are unit-selection based, which use standard speech databases available in neutral adult voices. Here reduced memory is achieved by the concatenation of phonemes and by replacing phonetic wave files with their LPC coefficients. Linguistic analysis was used to reduce the algorithmic complexity instead of signal processing techniques. Sufficient degree of customization and generalization catering to the needs of the child user had been included through the provision for vocabulary and voice selection to suit the requisites of the child. Prosody had also been incorporated. This inexpensive TTS system was implemented in MATLAB, with the synthesis presented by means of a graphical user interface (GUI), thus making it child friendly. This can be used not only as an interesting language learning aid for the normal child but it also serves as a speech aid to the vocally disabled child. The quality of the synthesized speech was evaluated using the mean opinion score (MOS).

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Cao, Yanlong, Yuanfeng He, Huawen Zheng, and Jiangxin Yang. "An Alarm Method for a Loose Parts Monitoring System." Shock and Vibration 19, no. 4 (2012): 753–61. http://dx.doi.org/10.1155/2012/891085.

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In order to reduce the false alarm rate and missed detection rate of a Loose Parts Monitoring System (LPMS) for Nuclear Power Plants, a new hybrid method combining Linear Predictive Coding (LPC) and Support Vector Machine (SVM) together to discriminate the loose part signal is proposed. The alarm process is divided into two stages. The first stage is to detect the weak burst signal for reducing the missed detection rate. Signal is whitened to improve the SNR, and then the weak burst signal can be detected by checking the short-term Root Mean Square (RMS) of the whitened signal. The second stage is to identify the detected burst signal for reducing the false alarm rate. Taking the signal's LPC coefficients as its characteristics, SVM is then utilized to determine whether the signal is generated by the impact of a loose part. The experiment shows that whitening the signal in the first stage can detect a loose part burst signal even at very low SNR and thusly can significantly reduce the rate of missed detection. In the second alarm stage, the loose parts' burst signal can be distinguished from pulse disturbance by using SVM. Even when the SNR is −15 dB, the system can still achieve a 100% recognition rate

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Roy, Sujan Kumar, and Kuldip K. Paliwal. "Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement." Signals 2, no. 3 (July 12, 2021): 434–55. http://dx.doi.org/10.3390/signals2030027.

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Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

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Treigys, Povilas, and Antanas Lipeika. "INVESTIGATION OF THE SPEAKER IDENTIFICATION METHOD BASED ON CLUSTERED PSEUDOSTATIONARY SEGMENTS OF VOICED SOUNDS." Technological and Economic Development of Economy 12, no. 1 (March 31, 2006): 50–55. http://dx.doi.org/10.3846/13928619.2006.9637722.

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The problem of speaker identification is investigated. Basic segments ‐ pseudo stationary intervals of voiced sounds are used for identification. The identification is carried out, comparing average distances between an investigative and comparatives. The coefficients of the linear prediction model (LPC) of a vocal tract are used as features of identification. Such a problem arises in stenographic practice where it is important for speech identification to know who is speaking. Identification should be used in stenography and it has to be fast enough in order not to disturb the stenographer's job. The clustered parameter data will be investigated by providing the performance of the speaker identification method with respect to the computational time and the number of errors.

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Nematollahi, Mohammad Ali, Chalee Vorakulpipat, and Hamurabi Gamboa Rosales. "Semifragile Speech Watermarking Based on Least Significant Bit Replacement of Line Spectral Frequencies." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/3597695.

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There are various techniques for speech watermarking based on modifying the linear prediction coefficients (LPCs); however, the estimated and modified LPCs vary from each other even without attacks. Because line spectral frequency (LSF) has less sensitivity to watermarking than LPC, watermark bits are embedded into the maximum number of LSFs by applying the least significant bit replacement (LSBR) method. To reduce the differences between estimated and modified LPCs, a checking loop is added to minimize the watermark extraction error. Experimental results show that the proposed semifragile speech watermarking method can provide high imperceptibility and that any manipulation of the watermark signal destroys the watermark bits since manipulation changes it to a random stream of bits.

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Manteuffel, G., and P. C. Schön. "STREMODO, ein innovatives Verfahren zur kontinuierlichen Erfassung der Stressbelastung von Schweinen bei Haltung und Transport." Archives Animal Breeding 47, no. 2 (October 10, 2004): 173–81. http://dx.doi.org/10.5194/aab-47-173-2004.

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Abstract. Title of the paper: STREMODO, an innovative technique for continuous stress assessment of pigs in housing and transport Vocal utterances of animals are the results of emotional states in specific situations. Therefore, distress calls of pigs can be used as indicators of impaired welfare. An automatic system was developed that responds selectively to stress vocalisations and that registrates and records their amount in the time domain. It can be applied in housing systems, during transports and in abattoirs. The patented technique is based on sequential records of the actual sound events in short time windows (92ms) and a parsimonious coding by 12 complex parameters (LPC-coefficients). A subsequent artificial neural network trained with respective parameters from porcine stress vocalisations is able to detect stress utterances with an error rate of less than 5 % even in noisy stables.

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Ahmed, Ahmed M., and Aliaa K. Hassan. "Speaker Recognition Systems in the Last Decade – A Survey." Engineering and Technology Journal 39, no. 1B (March 25, 2021): 30–40. http://dx.doi.org/10.30684/etj.v39i1b.1589.

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Speaker Recognition Defined by the process of recognizing a person by his\her voice through specific features that extract from his\her voice signal. An Automatic Speaker recognition (ASP) is a biometric authentication system. In the last decade, many advances in the speaker recognition field have been attained, along with many techniques in feature extraction and modeling phases. In this paper, we present an overview of the most recent works in ASP technology. The study makes an effort to discuss several modeling ASP techniques like Gaussian Mixture Model GMM, Vector Quantization (VQ), and Clustering Algorithms. Also, several feature extraction techniques like Linear Predictive Coding (LPC) and Mel frequency cepstral coefficients (MFCC) are examined. Finally, as a result of this study, we found MFCC and GMM methods could be considered as the most successful techniques in the field of speaker recognition so far.

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Müller-Enoch, Dieter, Robert Fintelmann, Andrey Nicolaev, and Hans Gruler. "Enzyme Activity of the Cytochrome P-450 Monooxygenase System in the Presence of Single Chain Lipid Molecules." Zeitschrift für Naturforschung C 56, no. 11-12 (December 1, 2001): 1082–90. http://dx.doi.org/10.1515/znc-2001-11-1227.

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Abstract The influence of single chain lipids on the 7-ethoxycoumarin O-deethyase activity of the reconstituted binary protein complex of isolated cytochrome P450 and NADPH-cytochrome P450 reductase has been examined. The enzyme activity of this binary enzyme complex has been shown to be influenced by (i) altering the complexation process of both proteins, (ii) by altering the catalytic cycle time of the active binary protein complex and (iii) by altering the fraction of substrate molecules at the catalytic center of the enzyme. Competitive inhibition was measured for all single chain molecules. The following dissociation coefficients of sub strate and lipids used for the catalytic center of the protein were obtained: 110 μм 7-ethoxy-coumarin (substrate), 1.1 μм MOG (1-monooleoyl-rac-glycerol), 0.3 μм SPH (D-sphingosine), 1.5 μм OA (oleic acid), 3.0 μм LPC (L-a-lysophosphatidyl-choline), 15.5 μм MSG (1-mono-stearoyl-rac-glycerol), 9.5 μм AA (arachidonic acid), 9.0 μм PaCar (palmitoyl-L-carnitine), 3.5 μм MPG (2-monopalmitoyl-glycerol), 1.5 μм LPI (L-a-lysophosphatidyl-inositol), 50 μм LA (lauric acid), 60 μм MA (myristic acid). 85 μм PA (palmitic acid), >100 μм SA (stearic acid). Only competitive inhibition with the substrate molecule 7-ethoxycoumarin was observed for the single chain lipids LA, MA, PA, SPH, SA, and OA. Non-competitive effects were observed for MPG (-0.03 μм-1) , PaCar (-0.02 μм-1) , MSG (-0.023 μм-1) , LPC (-0.03 μм-1) , A A (-0.03 μм-1) , and MOG (+0.04 μм-1). The negative sign indicates that the cycle time of the working binary complex is enlarged. The positive sign indicates that the formation of the binary complex is enhanced by MOG.

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Leinhos, Dirk C., Norbert R. Schmid, and Leonhard Fottner. "The Influence of Transient Inlet Distortions on the Instability Inception of a Low-Pressure Compressor in a Turbofan Engine." Journal of Turbomachinery 123, no. 1 (February 1, 2000): 1–8. http://dx.doi.org/10.1115/1.1330271.

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While studies on compressor flow instabilities under the presence of inlet distortions have been carried out with steady distortions in the past, the investigation presented here focuses on the influence of transient inlet distortions as generated by variable geometry engine intakes of super- and hypersonic aircraft on the characteristic and the nature of the instability inception of a LPC. The flow patterns (total pressure distortion with a superimposed co- or counterrotating swirl) of the distortions are adopted from a hypersonic concept aircraft. A LARZAC 04 twin-spool turbofan was operated with transient inlet distortions, generated by a moving delta wing, and steady total pressure distortions starting close to the LPC’s stability limit until it stalled. High-frequency pressure signals are recorded at different engine power settings. Instabilities are investigated with regard to the inception process and the early detection of stall precursors for providing data for a future stability control device. It turned out that the transient distortion does not have an influence on the surge margin of the LPC compared to the steady distortion, but that it changes the nature of stall inception. The pressure traces are analyzed in the time and frequency domain and also with tools like Spatial FFT, Power Spectral Density, and Traveling Wave Energy. A Wavelet Transformation algorithm is applied as well. While in the case of clean inlet flow, the compressor exhibits different types of stall inception depending on the engine speed, stall is always initiated by spike-type disturbances under the presence of steady or transient distortions. Modal disturbances are present in the mid-speed range that do not grow into stall, but rather interact with the inlet flow and produce short length scale disturbances. The obtained early warning times prior to stall are adversely affected by transient distortions in some cases. The problem of appropriate thresholding becomes evident. The best warning times have been acquired using a statistical evaluation of the Wavelet coefficients, which might be promising to apply in a staged active control system. This system could include different phases of detection and actuation depending on the current precursor.

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Xue, Yuqun, Zhijiu Zhu, Jianhua Jiang, Yi Zhan, Zenghui Yu, Xiaohua Fan, and Shushan Qiao. "Fast Computation of LSP Frequencies Using the Bairstow Method." Electronics 9, no. 3 (February 26, 2020): 387. http://dx.doi.org/10.3390/electronics9030387.

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Linear prediction is the kernel technology in speech processing. It has been widely applied in speech recognition, synthesis, and coding, and can efficiently and correctly represent the speech frequency spectrum with only a few parameters. Line Spectrum Pairs (LSPs) frequencies, as an alternative representation of Linear Predictive Coding (LPC), have the advantages of good quantization accuracy and low spectral sensitivity. However, computing the LSPs frequencies takes a long time. To address this issue, a fast computation algorithm, based on the Bairstow method for computing LSPs frequencies from linear prediction coefficients, is proposed in this paper. The algorithm process first transforms the symmetric and antisymmetric polynomial to general polynomial, then extracts the polynomial roots. Associated with the short-term stationary property of speech signal, an adaptive initial method is applied to reduce the average iteration numbers by 26%, as compared to the statics in the initial method, with a Perceptual Evaluation of Speech Quality (PESQ) score reaching 3.46. Experimental results show that the proposed method can extract the polynomial roots efficiently and accurately with significantly reduced computation complexity. Compared to previous works, the proposed method is 17 times faster than Tschirnhus Transform, and has a 22% PESQ improvement on the Birge-Vieta method with an almost comparable computation time.

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Soto-Murillo, Manuel A., Jorge I. Galván-Tejada, Carlos E. Galván-Tejada, Jose M. Celaya-Padilla, Huizilopoztli Luna-García, Rafael Magallanes-Quintanar, Tania A. Gutiérrez-García, and Hamurabi Gamboa-Rosales. "Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods." Healthcare 9, no. 3 (March 12, 2021): 317. http://dx.doi.org/10.3390/healthcare9030317.

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The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.

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Yuan, Yuan, Jiyuan Zheng, Ann K. Rockwell, Stephen D. March, Seth R. Bank, and Joe C. Campbell. "AlInAsSb Impact Ionization Coefficients." IEEE Photonics Technology Letters 31, no. 4 (February 15, 2019): 315–18. http://dx.doi.org/10.1109/lpt.2019.2894114.

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Kim, Yeong Gu. "A Study on the Partition Coefficients for Sulfur Compounds Related Composition of LPG." Journal of the Korean Chemical Society 46, no. 6 (December 20, 2002): 523–27. http://dx.doi.org/10.5012/jkcs.2002.46.6.523.

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38

Vinçon-Leite, B., J. M. Mouchel, and B. Tassin. "Modélisation de l'évolution thermique saisonnière du lac du Bourget." Revue des sciences de l'eau 2, no. 4 (April 12, 2005): 483–510. http://dx.doi.org/10.7202/705040ar.

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Le lac du Bourget, l'un des principaux lacs alpins situé en France, a fait l'objet en 1981 d'importants travaux d'aménagement en vue de ta restauration de la qualité de ses eaux. Une campagne de mesure portant sur les années 1988-89 a été mise en place afin de faire le point sur l'évolution du lac depuis la fin des travaux. Un modèle thermique et biogéochimique (phosphore, oxygène, azote) sera utilisé pour synthétiser les connaissances, prévoir l'évolution de la qualité des eaux du lac ainsi que l'influence d'éventuels aménagements complémentaires. Les résultats présentés ici concernent la première étape du projet d'études, la modélisation thermique du lac du Bourget. Le modèle utilisé est un modèle unidimensionnel, vertical, basé sur l'équation d'advection-diffusion. L'expression des coefficients de dispersion selon la profondeur reprend celle d'un modèle du lac Léman (Tassin, 1986). Les équations utilisées distinguent l'épilimnion, le métalimnion et l'hypolimnion. Les résultats présentés montrent que le modèle décrit de façon satisfaisante le cycle thermique annuel et l'évolution inter-annuelle des températures dans le lac du Bourget. Les profils et les valeurs des coefficients de dispersion calculés sur le lac du Bourget sont proches de ceux obtenus sur d'autres lacs à partir de mesures fines de température ou de concentrations d'isotopes naturels. Les coefficients de dispersion obtenus pourront donc être utilisés dans la modélisation des substances dissoutes dans le lac.

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Li, Fenling, Li Wang, Jing Liu, Yuna Wang, and Qingrui Chang. "Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis." Remote Sensing 11, no. 11 (June 3, 2019): 1331. http://dx.doi.org/10.3390/rs11111331.

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Leaf nitrogen concentration (LNC) is an important indicator for accurate diagnosis and quantitative evaluation of plant growth status. The objective was to apply a discrete wavelet transform (DWT) analysis in winter wheat for the estimation of LNC based on visible and near-infrared (400–1350 nm) canopy reflectance spectra. In this paper, in situ LNC data and ground-based hyperspectral canopy reflectance was measured over three years at different sites during the tillering, jointing, booting and filling stages of winter wheat. The DWT analysis was conducted on canopy original spectrum, log-transformed spectrum, first derivative spectrum and continuum removal spectrum, respectively, to obtain approximation coefficients, detail coefficients and energy values to characterize canopy spectra. The quantitative relationships between LNC and characteristic parameters were investigated and compared with models established by sensitive band reflectance and typical spectral indices. The results showed combining log-transformed spectrum and a sym8 wavelet function with partial least squares regression (PLS) based on the approximation coefficients at decomposition level 4 most accurately predicted LNC. This approach could explain 11% more variability in LNC than the best spectral index mSR705 alone, and was more stable in estimating LNC than models based on random forest regression (RF). The results indicated that narrowband reflectance spectroscopy (450–1350 nm) combined with DWT analysis and PLS regression was a promising method for rapid and nondestructive estimation of LNC for winter wheat across a range in growth stages.

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40

Ghiasi, Behzad, Hossein Sheikhian, Amin Zeynolabedin, and Mohammad Hossein Niksokhan. "Granular computing–neural network model for prediction of longitudinal dispersion coefficients in rivers." Water Science and Technology 80, no. 10 (November 15, 2019): 1880–92. http://dx.doi.org/10.2166/wst.2020.006.

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Abstract Successful application of one-dimensional advection–dispersion models in rivers depends on the accuracy of the longitudinal dispersion coefﬁcient (LDC). In this regards, this study aims to introduce an appropriate approach to estimate LDC in natural rivers that is based on a hybrid method of granular computing (GRC) and an artificial neural network (ANN) model (GRC-ANN). Also, adaptive neuro-fuzzy inference system (ANFIS) and ANN models were developed to investigate the accuracy of three credible artificial intelligence (AI) models and the performance of these models in different LDC values. By comparing with empirical models developed in other studies, the results revealed the superior performance of GRC-ANN for LDC estimation. The sensitivity analysis of the three intelligent models developed in this study was done to determine the sensitivity of each model to its input parameters, especially the most important ones. The sensitivity analysis results showed that the W/H parameter (W: channel width; H: flow depth) has the most significant impact on the output of all three models in this research.

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Vidal, B., J. L. Corral, and J. Marti. "All-optical WDM microwave filter with negative coefficients." IEEE Photonics Technology Letters 17, no. 3 (March 2005): 666–68. http://dx.doi.org/10.1109/lpt.2004.840925.

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42

Esfahani, Zahra, Majid Roohi, Meysam Gheisarnejad, Tomislav Dragičević, and Mohammad-Hassan Khooban. "Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids." Applied Sciences 9, no. 16 (August 19, 2019): 3411. http://dx.doi.org/10.3390/app9163411.

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In this paper, the concept of fractional calculus (FC) is introduced into the sliding mode control (SMC), named fractional order SMC (FOSMC), for the load frequency control (LFC) of an islanded microgrid (MG). The studied MG is constructed from different autonomous generation components such as diesel engines, renewable sources, and storage devices, which are optimally planned to benefit customers. The coefficients embedded in the FOSMC structure play a vital role in the quality of controller commands, so there is a need for a powerful heuristic methodology in the LFC study to adjust the design coefficients in such a way that better transient output may be achieved for resistance to renewable sources fluctuations. Accordingly, the Sine Cosine algorithm (SCA) is effectively combined with the harmony search (HS) for the optimal setting of the controller coefficients. The Lyapunov function based on the FOSMC is formulated to guarantee the stability of the LFC mechanism for the test MG. Finally, the hardware-in-the-loop (HIL) experiments are carried out to ensure that the suggested controller can suppress the frequency fluctuations effectively, and that it provides more robust MG responses in comparison with the prior art techniques.

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Kolli, Kranthi K., R. K. Banerjee, Srikara V. Peelukhana, T. A. Helmy, M. A. Leesar, Imran Arif, E. W. Schneeberger, et al. "Influence of heart rate on fractional flow reserve, pressure drop coefficient, and lesion flow coefficient for epicardial coronary stenosis in a porcine model." American Journal of Physiology-Heart and Circulatory Physiology 300, no. 1 (January 2011): H382—H387. http://dx.doi.org/10.1152/ajpheart.00412.2010.

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A limitation in the use of invasive coronary diagnostic indexes is that fluctuations in hemodynamic factors such as heart rate (HR), blood pressure, and contractility may alter resting or hyperemic flow measurements and may introduce uncertainties in the interpretation of these indexes. In this study, we focused on the effect of fluctuations in HR and area stenosis (AS) on diagnostic indexes. We hypothesized that the pressure drop coefficient (CDPe, ratio of transstenotic pressure drop and distal dynamic pressure), lesion flow coefficient (LFC, square root of ratio of limiting value CDP and CDP at site of stenosis) derived from fluid dynamics principles, and fractional flow reserve (FFR, ratio of average distal and proximal pressures) are independent of HR and can significantly differentiate between the severity of stenosis. Cardiac catheterization was performed on 11 Yorkshire pigs. Simultaneous measurements of distal coronary arterial pressure and flow were performed using a dual sensor-tipped guidewire for HR < 120 and HR > 120 beats/min, in the presence of epicardial coronary lesions of <50% AS and >50% AS. The mean values of FFR, CDPe, and LFC were significantly different ( P < 0.05) for lesions of <50% AS and >50% AS (0.88 ± 0.04, 0.76 ± 0.04; 62 ± 30, 151 ± 35, and 0.10 ± 0.02 and 0.16 ± 0.01, respectively). The mean values of FFR and CDPe were not significantly different ( P > 0.05) for variable HR conditions of HR < 120 and HR > 120 beats/min (FFR, 0.81 ± 0.04 and 0.82 ± 0.04; and CDPe, 95 ± 33 and 118 ± 36). The mean values of LFC do somewhat vary with HR (0.14 ± 0.01 and 0.12 ± 0.02). In conclusion, fluctuations in HR have no significant influence on the measured values of CDPe and FFR but have a marginal influence on the measured values of LFC. However, all three parameters can significantly differentiate between stenosis severities. These results suggest that the diagnostic parameters can be potentially used in a better assessment of coronary stenosis severity under a clinical setting.

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44

Xiao, Yin, Lina Zhou, and Wen Chen. "Single-Pixel Imaging Authentication Using Sparse Hadamard Spectrum Coefficients." IEEE Photonics Technology Letters 31, no. 24 (December 15, 2019): 1975–78. http://dx.doi.org/10.1109/lpt.2019.2952177.

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45

Shuto, Y., S. Yanagi, S. Asakawa, M. Kobayashi, and R. Nagase. "Evaluation of High-Temperature Absorption Coefficients of Optical Fibers." IEEE Photonics Technology Letters 16, no. 4 (April 2004): 1008–10. http://dx.doi.org/10.1109/lpt.2004.824633.

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46

Vidal, B., V. Polo, J. L. Corral, and J. Marti. "Photonic Microwave Filter With Negative Coefficients Based on WDM Techniques." IEEE Photonics Technology Letters 16, no. 9 (September 2004): 2123–25. http://dx.doi.org/10.1109/lpt.2004.833086.

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47

Kaur, Manpreet, Neelam Rup Prakash, Parveen Kalra, Goverdhan Dutt Puri, Tanvir Samra, and Manoj Goyal. "Variations in Electrocortical Activity due to Surgical Incision in Anaesthetized Cardiac Patients: Electroencephalogram-Based Quantitative Analysis." Journal of Healthcare Engineering 2020 (March 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/4643584.

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This study examines the alterations in scalp recorded cortical activity due to surgical incision in anaesthetized cardiac patients using electroencephalogram (EEG) patterns. The primary aim was to compare the changes in electrocortical activity after surgical incision. The secondary aim was to compare the changes in time, frequency, and wavelet domain parameters after loss of consciousness (LoC) and after intubation. Real-time EEG data were recorded from 19 patients undergoing cardiac surgery and signals were quantified with time, frequency, and wavelet domain parameters. An increase in hjorth activity, hjorth complexity, rms value, total band power, relative delta band power, standard deviation and maxima of approximation coefficients (a5), minima of detail coefficients (d5, d4, and d3) and decrease in hjorth mobility; approximate entropy; relative theta, alpha, and beta band power; specentropy; median, spectral edge, and mean frequency; mean of detail coefficients (d4); standard deviation of detail coefficients (d5, d4, and d3); maxima of detail coefficients (d5); and minima of approximation coefficients (a5) were observed during LoC. Decrease in hjorth activity; hjorth mobility; rms value; total band power; relative theta band power; median frequency; standard deviation of coefficients (a5, d5, d4, and d3); and maxima of coefficients (a5, d5, d4, and d3) and increase in hjorth complexity; mean of detail coefficients (d5); and minima of coefficients (a5, d5, d4, and d3) were observed after intubation. Significant decrease in hjorth activity; hjorth mobility; total band power; relative alpha band power; specentropy; median and mean frequency; standard deviation and maxima of detail coefficients (d5, d4, and d3) and increase in rms value; relative delta band power; mean of coefficients (a5 and d5); and minima of coefficients (d5, d4, and d3) were observed due to surgical incision. It can be concluded that different spectral and temporal parameters of EEG signals are highly sensitive to induction, intubation, and surgical incision which are potentially informative for measuring the depth of anaesthesia or efficacy of anaesthetic agents.

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Blankenship, P. D., M. C. Lamb, C. L. Butts, T. B. Whitaker, and E. J. Williams. "Grading High Moisture Farmer Stock Peanut Lots1." Peanut Science 28, no. 1 (January 1, 2001): 38–43. http://dx.doi.org/10.3146/i0095-3679-28-1-10.

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Abstract Farmers in the U.S. are required to market peanuts as identity preserved lots with less than or equal to 10.49% moisture content (MC) wet basis. A comparison of peanut grades, weights, and values at moisture contents above (Hmc) and below (Lmc) 10.49% was conducted at 16 buying points during crop year 1998 and at 22 points in 1999. Buying points were located in all three U.S. peanut-producing areas both years. Randomly selected Hmc lots of runner-, spanish-, and virgina-type peanuts were weighed and unofficially graded by Federal State Inspection Serv. personnel with standard procedures. Lots were cured to MC ≤ 10.49% and graded officially for farmer marketing. Data from both years were combined for analysis. Both Hmc and Lmc grades were conducted on 543, 62, and 81 runner-, spanish-, and virginia-type lots, respectively. Moisture contents for runner type averaged 16.3% at Hmc grading and 8.7% at Lmc grading; for spanish type, 15.8 and 8.7%; and for virginia type, 17.0 and 9.1%. Only 3.8% of all lots evaluated had Hmc moisture contents greater than 25%. Equations were derived that predicted Lmc grade factors, lot weights (LW) and lot values (LV) from measured Hmc factors by peanut type. Equations to estimate Lmc LW and LV for runner-type peanuts had correlation coefficients of 0.998 and 0.997, respectively. Correlation coefficients for spanish type Lmc LW and LV were 0.998 and 0.995 and for virginia type 0.996 and 0.993, respectively. Derived equations for Lmc grade factors, LW, and LV may offer an alternative modification in U.S. peanut grading and farmer marketing allowing an increase in the maximum MC at grading.

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Singh, Ranbir, Lokesh Kumar, Pawan Kumar Netrakanti, and Bedangadas Mohanty. "Selected Experimental Results from Heavy-Ion Collisions at LHC." Advances in High Energy Physics 2013 (2013): 1–22. http://dx.doi.org/10.1155/2013/761474.

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We review a subset of experimental results from the heavy-ion collisions at the Large Hadron Collider (LHC) facility at CERN. Excellent consistency is observed across all the experiments at the LHC (at center of mass energysNN=2.76 TeV) for the measurements such as charged particle multiplicity density, azimuthal anisotropy coefficients, and nuclear modification factor of charged hadrons. Comparison to similar measurements from the Relativistic Heavy Ion Collider (RHIC) at lower energy (sNN=200 GeV) suggests that the system formed at LHC has a higher energy density and larger system size and lives for a longer time. These measurements are compared to model calculations to obtain physical insights on the properties of matter created at the RHIC and LHC.

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Loayssa, A., J. Capmany, M. Sagues, and J. Mora. "Demonstration of incoherent microwave photonic filters with all-optical complex coefficients." IEEE Photonics Technology Letters 18, no. 16 (August 2006): 1744–46. http://dx.doi.org/10.1109/lpt.2006.879535.

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Journal articles on the topic 'LPC coefficients'

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