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Статті в журналах з теми "Mel-Frequency Cepstral Coefficients (MFCCs)"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

ARORA, SHRUTI, SUSHMA JAIN, and INDERVEER CHANA. "A FUSION FRAMEWORK BASED ON CEPSTRAL DOMAIN FEATURES FROM PHONOCARDIOGRAM TO PREDICT HEART HEALTH STATUS." Journal of Mechanics in Medicine and Biology 21, no. 04 (2021): 2150034. http://dx.doi.org/10.1142/s0219519421500342.

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A great increase in the number of cardiovascular cases has been a cause of serious concern for the medical experts all over the world today. In order to achieve valuable risk stratification for patients, early prediction of heart health can benefit specialists to make effective decisions. Heart sound signals help to know about the condition of heart of a patient. Motivated by the success of cepstral features in speech signal classification, authors have used here three different cepstral features, viz. Mel-frequency cepstral coefficients (MFCCs), gammatone frequency cepstral coefficients (GFCC

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2

H. Mohd Johari, N., Noreha Abdul Malik, and K. A. Sidek. "Distinctive features for normal and crackles respiratory sounds using cepstral coefficients." Bulletin of Electrical Engineering and Informatics 8, no. 3 (2019): 875–81. http://dx.doi.org/10.11591/eei.v8i3.1517.

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Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard d

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3

Eskidere, Ömer, and Ahmet Gürhanlı. "Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features." Computational and Mathematical Methods in Medicine 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/956249.

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The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The result

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4

Pratiwi, Tika, Andi Sunyoto, and Dhani Ariatmanto. "Music Genre Classification Using K-Nearest Neighbor and Mel-Frequency Cepstral Coefficients." Sinkron 8, no. 2 (2024): 861–67. http://dx.doi.org/10.33395/sinkron.v8i2.12912.

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Music genre classification plays a pivotal role in organizing and accessing vast music collections, enhancing user experiences, and enabling efficient music recommendation systems. This study focuses on employing the K-Nearest Neighbors (KNN) algorithm in conjunction with Mel-Frequency Cepstral Coefficients (MFCCs) for accurate music genre classification. MFCCs extract essential spectral features from audio signals, which serve as robust representations of music characteristics. The proposed approach achieves a commendable classification accuracy of 80%, showcasing the effectiveness of KNN-MFC

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5

Kasim, Anita Ahmad, Muhammad Bakri, Irwan Mahmudi, Rahmawati Rahmawati, and Zulnabil Zulnabil. "Artificial Intelligent for Human Emotion Detection with the Mel-Frequency Cepstral Coefficient (MFCC)." JUITA : Jurnal Informatika 11, no. 1 (2023): 47. http://dx.doi.org/10.30595/juita.v11i1.15435.

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Emotions are an important aspect of human communication. Expression of human emotions can be identified through sound. The development of voice detection or speech recognition is a technology that has developed rapidly to help improve human-machine interaction. This study aims to classify emotions through the detection of human voices. One of the most frequently used methods for sound detection is the Mel-Frequency Cepstrum Coefficient (MFCC) where sound waves are converted into several types of representation. Mel-frequency cepstral coefficients (MFCCs) are the coefficients that collectively

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6

Varma, V. Sai Nitin, and Abdul Majeed K.K. "Advancements in Speaker Recognition: Exploring Mel Frequency Cepstral Coefficients (MFCC) for Enhanced Performance in Speaker Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 88–98. http://dx.doi.org/10.22214/ijraset.2023.55124.

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Abstract: Speaker recognition, a fundamental capability of software or hardware systems, involves receiving speech signals, identifying the speaker present in the speech signal, and subsequently recognizing the speaker for future interactions. This process emulates the cognitive task performed by the human brain. At its core, speaker recognition begins with speech as the input to the system. Various techniques have been developed for speech recognition, including Mel frequency cepstral coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral coefficients (LPCC), Li

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7

N., H. Mohd Johari, Abdul Malik Noreha, and A. Sidek K. "Distinctive features for normal and crackles respiratory sounds using cepstral coefficients." Bulletin of Electrical Engineering and Informatics 8, no. 3 (2019): 875–81. https://doi.org/10.11591/eei.v8i3.1517.

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Анотація:

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard d

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8

ELSHARKAWY, R. R., M. HINDY, S. EL-RABAIE, and M. I. DESSOUKY. "FET SMALL-SIGNAL MODELING USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS AND THE DISCRETE COSINE TRANSFORM." Journal of Circuits, Systems and Computers 19, no. 08 (2010): 1835–46. http://dx.doi.org/10.1142/s0218126610007158.

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In this paper, a novel neural technique is proposed for FET small-signal modeling. This technique is based on the discrete cosine transform (DCT) and the Mel-frequency cepstral coefficients (MFCCs). The input data to traditional neural systems for FET small-signal modeling are the scattering parameters and the corresponding frequencies in a certain band, and the outputs are the circuit elements. In the proposed technique, the input data are considered random, and the MFCCs are calculated from these inputs and their DCT. The MFCCs are used to give a few features from the input random data seque

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9

Musab, T. S. Al-Kaltakchi, Abd Al-Raheem Taha Haithem, Abd Shehab Mohanad, and A. M. Abdullah Mohammed. "Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 2 (2020): 782–89. https://doi.org/10.11591/ijeecs.v18.i2.pp782-789.

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In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight dif

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10

Ma, Liqiang, Anqi Jiang, and Wanlu Jiang. "The Intelligent Diagnosis of a Hydraulic Plunger Pump Based on the MIGLCC-DLSTM Method Using Sound Signals." Machines 12, no. 12 (2024): 869. https://doi.org/10.3390/machines12120869.

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To fully exploit the rich state and fault information embedded in the acoustic signals of a hydraulic plunger pump, this paper proposes an intelligent diagnostic method based on sound signal analysis. First, acoustic signals were collected under normal and various fault conditions. Then, four distinct acoustic features—Mel Frequency Cepstral Coefficients (MFCCs), Inverse Mel Frequency Cepstral Coefficients (IMFCCs), Gammatone Frequency Cepstral Coefficients (GFCCs), and Linear Prediction Cepstral Coefficients (LPCCs)—were extracted and integrated into a novel hybrid cepstral feature called MIG

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11

P, S. Subhashini Pedalanka, SatyaSai Ram M, and Sreenivasa Rao Duggirala. "Mel Frequency Cepstral Coefficients based Bacterial Foraging Optimization with DNN-RBF for Speaker Recognition." Indian Journal of Science and Technology 14, no. 41 (2021): 3082–92. https://doi.org/10.17485/IJST/v14i41.1858.

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<strong>Objectives:</strong> To improve the accuracy and to reduce the time complexity of the Speaker Recognition system using Mel-Frequency Cepstral Coefficients (MFCCs) and Bacterial Foraging optimization (BFO) with DNN –RBF. <strong>Method:</strong> The MFCCs of each speech sample are derived by pre-processing the audio speech signal. The features are optimized with BFO algorithm. Finally, the probability score for each speaker is generated to identify the speaker. Then the features are classified towards the target speaker using DNN-RBF. For the proposed MBFOB speaker

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12

PROF., MANTRI D.B. "IMPLEMENTATION OF SPEECH RECOGNITION SYSTEM." IJIERT - International Journal of Innovations in Engineering Research and Technology 3, no. 12 (2016): 72–80. https://doi.org/10.5281/zenodo.1462451.

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<strong>Speech recognition is an important and active analysis area of the recent years. This analysis aims to make a system for speech recognition with the help of dynamic time wrapping algorithm program,by examining the speech signal of the speaker with pre - stored speech signals with in the stored database,and extracting by using Mel - frequency Cepstral coefficients which is the main features of the speaker speech signal and one of the most necessary factors in achieving high recognition accuracy. The process of extraction and matching is implemented after the Pre Process or filtering sig

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13

Al-Kaltakchi, Musab T. S., Haithem Abd Al-Raheem Taha, Mohanad Abd Shehab, and Mohamed A. M. Abdullah. "Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (2020): 782. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp782-789.

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Анотація:

<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the M

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14

Naveena, V., Susmitha Vekkot, and K. Jeeva Priya. "Voice Conversion System Based on Deep Neural Networks." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 316–21. http://dx.doi.org/10.1166/jctn.2020.8668.

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The paper focuses on usage of deep neural networks for converting a person’s voice to another person’s voice, analogous to a mimic. The work in this paper introduces the concept of neural networks and deploys multi-layer deep neural networks for building a framework for voice conversion. The spectral Mel-Frequency Cepstral Coefficients (MFCCs) are converted using a 10-layer deep network while fundamental frequency (F0) conversion is accomplished by logarithmic Gaussian normalized transformation. MFCCs are subjected to inverse cepstral filtering while changes in F0 are incorporated using Pitch

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15

Zainal, Nur Aishah, Ani Liza Asnawi, Siti Noorjannah Ibrahim, Nor Fadhillah Mohamed Azmin, Norharyati Harum, and Nora Mat Zin. "Utilizing MFCCs and TEO-MFCCs to Classify Stress in Females Using SSNNA." IIUM Engineering Journal 26, no. 1 (2025): 324–35. https://doi.org/10.31436/iiumej.v26i1.3411.

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All individuals are susceptible to experiencing stress in their everyday lives. Nevertheless, stress has a greater influence on females due to both biological and environmental factors. This study utilized female speeches to detect and classify stress and no stress in women. Using speech, composed of non-invasive and non-intrusive approaches, helps to identify stress better in females. A comparative analysis was conducted between Mel-frequency Cepstral Coefficients (MFCCs) and Teager Energy Operator- MFCCs (TEO-MFCCs) to determine the best speech feature for classifying emotions associated wit

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16

Prajapati, Pooja, and Miral Patel. "Feature Extraction of Isolated Gujarati Digits with Mel Frequency Cepstral Coefficients (MFCCs)." International Journal of Computer Applications 163, no. 6 (2017): 29–33. http://dx.doi.org/10.5120/ijca2017913551.

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17

Civera, Marco, Matteo Ferraris, Rosario Ceravolo, Cecilia Surace, and Raimondo Betti. "The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool." Applied Sciences 9, no. 23 (2019): 5064. http://dx.doi.org/10.3390/app9235064.

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Recently, features and techniques from speech processing have started to gain increasing attention in the Structural Health Monitoring (SHM) community, in the context of vibration analysis. In particular, the Cepstral Coefficients (CCs) proved to be apt in discerning the response of a damaged structure with respect to a given undamaged baseline. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs). This approach, while efficient and still very common in applications, such as speech and speaker recognition, has been followed by other more advanced and competitive techniques

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18

Thakur, Surendra, Emmanuel Adetiba, Oludayo O. Olugbara, and Richard Millham. "Experimentation Using Short-Term Spectral Features for Secure Mobile Internet Voting Authentication." Mathematical Problems in Engineering 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/564904.

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We propose a secure mobile Internet voting architecture based on the Sensus reference architecture and report the experiments carried out using short-term spectral features for realizing the voice biometric based authentication module of the architecture being proposed. The short-term spectral features investigated are Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Frequency Discrete Wavelet Coefficients (MFDWC), Linear Predictive Cepstral Coefficients (LPCC), and Spectral Histogram of Oriented Gradients (SHOGs). The MFCC, MFDWC, and LPCC usually have higher dimensions that oftentimes lead t

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19

Abdul, Zrar Khalid. "Kurdish Spoken Letter Recognition based on k-NN and SVM Model." Journal of University of Raparin 7, no. 4 (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 sample

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20

Kasture,, Rajlaxmi. "Bird Sound Prediction." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04157.

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ABSTRACT - This paper presents a machine learning-based system designed for recognizing bird species from their vocalizations. Leveraging Artificial Neural Networks (ANNs), specifically a Multilayer Perceptron (MLP), the system processes pre-recorded bird calls, extracts Mel Frequency Cepstral Coefficients (MFCCs), and classifies the sound using a trained ANN model. The proposed system is computationally efficient and accurate, making it a valuable tool for ecological research and conservation. The system is deployed via a user-friendly Flask web interface for real-time usage. Keywords: Bird S

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21

Hosseinzadeh, Mehdi, Amir Haider, Mazhar Hussain Malik, et al. "Enhanced heart sound classification using Mel frequency cepstral coefficients and comparative analysis of single vs. ensemble classifier strategies." PLOS ONE 19, no. 12 (2024): e0316645. https://doi.org/10.1371/journal.pone.0316645.

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This paper seeks to enhance the performance of Mel Frequency Cepstral Coefficients (MFCCs) for detecting abnormal heart sounds. Heart sounds are first pre-processed to remove noise and then segmented into S1, systole, S2, and diastole intervals, with thirteen MFCCs estimated from each segment, yielding 52 MFCCs per beat. Finally, MFCCs are used for heart sound classification. For that purpose, a single classifier and an innovative ensemble classifier strategy are presented and compared. In the single classifier strategy, the MFCCs from nine consecutive beats are averaged to classify heart soun

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22

Wulandari Siagian, Thasya Nurul, Hilal Hudan Nuha, and Rahmat Yasirandi. "Footstep Recognition Using Mel Frequency Cepstral Coefficients and Artificial Neural Network." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 3 (2020): 497–503. http://dx.doi.org/10.29207/resti.v4i3.1964.

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Footstep recognition is relatively new biometrics and based on the learning of footsteps signals captured from people walking on the sensing area. The footstep signals classification process for security systems still has a low level of accuracy. Therefore, we need a classification system that has a high accuracy for security systems. Most systems are generally developed using geometric and holistic features but still provide high error rates. In this research, a new system is proposed by using the Mel Frequency Cepstral Coefficients (MFCCs) feature extraction, because it has a good linear fre

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23

Mohammed, Duraid Y., Khamis Al-Karawi, and Ahmed Aljuboori. "Robust speaker verification by combining MFCC and entrocy in noisy conditions." Bulletin of Electrical Engineering and Informatics 10, no. 4 (2021): 2310–19. http://dx.doi.org/10.11591/eei.v10i4.2957.

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Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, wit

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24

Duraid, Y. Mohammed, Al-Karawi Khamis, and Aljuboori Ahmed. "Robust speaker verification by combining MFCC and entrocy in noisy conditions." Bulletin of Electrical Engineering and Informatics 10, no. 4 (2021): pp. 2310~2319. https://doi.org/10.11591/eei.v10i4.2957.

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Анотація:

Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, wit

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25

Borawake, Madhuri. "Deep Fake Audio Recognition Using Deep Learning." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03689.

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Abstract - Deep fake audio is incredibly lifelike synthetic audio that can be produced because to recent advancements in deep learning algorithms. This poses a major threat to digital communications' legitimacy, security, and privacy. Deep fake audio detection has become a critical challenge since current techniques cannot keep up with the rapid advancements in audio synthesis technology. The objective of this study is to develop a dependable deep fake audio detection system using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. The proposed method consistently dist

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26

Patil, Adwait. "Covid Classification Using Audio Data." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1633–37. http://dx.doi.org/10.22214/ijraset.2021.38675.

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Abstract: Coronavirus outbreak has affected the entire world adversely this project has been developed in order to help common masses diagnose their chances of been covid positive just by using coughing sound and basic patient data. Audio classification is one of the most interesting applications of deep learning. Similar to image data audio data is also stored in form of bits and to understand and analyze this audio data we have used Mel frequency cepstral coefficients (MFCCs) which makes it possible to feed the audio to our neural network. In this project we have used Coughvid a crowdsource

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27

Jafari, Ayyoob. "CLASSIFICATION OF PARKINSON'S DISEASE PATIENTS USING NONLINEAR PHONETIC FEATURES AND MEL-FREQUENCY CEPSTRAL ANALYSIS." Biomedical Engineering: Applications, Basis and Communications 25, no. 04 (2013): 1350001. http://dx.doi.org/10.4015/s1016237213500014.

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This paper presents a combinational feature extraction approach using voice utterances for discriminating Parkinson's disease (PD) patients from healthy people. The proposed feature set consists of seven nonlinear phonetic features and 13 usual Mel-frequency cepstral coefficients (MFCCs). In this research, two new features — EDC-PIS (energy distribution coefficient of peak index series) and EDC-PMS (energy distribution coefficient of peak magnitude series) — were introduced, which are robust to many uncontrollable confounding effects such as noisy environments. The nonlinear phonetic features

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28

Jokić, Ivan, Stevan Jokić, Vlado Delić, and Zoran Perić. "One Solution of Extension of Mel-Frequency Cepstral Coefficients Feature Vector for Automatic Speaker Recognition." Information Technology And Control 49, no. 2 (2020): 224–36. http://dx.doi.org/10.5755/j01.itc.49.2.22258.

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Анотація:

One extension of feature vector for automatic speaker recognition is considered in this paper. The starting feature vector consisted of 18 mel-frequency cepstral coefficients (MFCCs). Extension was done with two additional features derived from the spectrum of the speech signal. The main idea that generated this research is that it is possible to increase the efficiency of automatic speaker recognition by constructing a feature vector which tracks a real perceived spectrum in the observed speech. Additional features are based on the energy maximums in the appropriate frequency ranges of observ

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29

Al-Karawi, Khamis A. "Robustness Speaker Recognition Based on Feature Space in Clean and Noisy Condition." International Journal of Sensors, Wireless Communications and Control 9, no. 4 (2019): 497–506. http://dx.doi.org/10.2174/2210327909666181219143918.

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Анотація:

Background & Objective: Speaker Recognition (SR) techniques have been developed into a relatively mature status over the past few decades through development work. Existing methods typically use robust features extracted from clean speech signals, and therefore in idealized conditions can achieve very high recognition accuracy. For critical applications, such as security and forensics, robustness and reliability of the system are crucial. Methods: The background noise and reverberation as often occur in many real-world applications are known to compromise recognition performance. To improv

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30

Iqbal, Kashif. "Performance Evaluation of Environmental Sound Classification: A Machine Learning Stacking and Multi-Criteria Metrics Based Approach." Volume 21, Issue 1 21, no. 1 (2023): 77–86. http://dx.doi.org/10.52584/qrj.2101.10.

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This study proposes an Environment Sound Classification Task (ESC) model that includes numerous element channels given as a contribution to Machine learning with an Attention instrument. ESC is a significant testing issue. The interest in the paper lies in utilizing different part channels involving the MFCCs-Mel Frequency Cepstral Coefficients a mutual module in speaker detection and artificial speech systems. LPCs-Linear Prediction Coefficients and Linear Prediction Cepstral Coefficients were the most commonly used types in ASR- Automated speech recognition. The paper also discusses some bas

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31

Lalitha, S., and Deepa Gupta. "An Encapsulation of Vital Non-Linear Frequency Features for Various Speech Applications." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 303–7. http://dx.doi.org/10.1166/jctn.2020.8666.

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Анотація:

Mel Frequency Cepstral Coefficients (MFCCs) and Perceptual linear prediction coefficients (PLPCs) are widely casted nonlinear vocal parameters in majority of the speaker identification, speaker and speech recognition techniques as well in the field of emotion recognition. Post 1980s, significant exertions are put forth on for the progress of these features. Considerations like the usage of appropriate frequency estimation approaches, proposal of appropriate filter banks, and selection of preferred features perform a vital part for the strength of models employing these features. This article p

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INDRAWATY, YOULLIA, IRMA AMELIA DEWI, and RIZKI LUKMAN. "Ekstraksi Ciri Pelafalan Huruf Hijaiyyah Dengan Metode Mel-Frequency Cepstral Coefficients." MIND Journal 4, no. 1 (2019): 49–64. http://dx.doi.org/10.26760/mindjournal.v4i1.49-64.

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Huruf hijaiyyah merupakan huruf penyusun ayat dalam Al Qur’an. Setiap hurufhijaiyyah memiliki karakteristik pelafalan yang berbeda. Tetapi dalam praktiknya,ketika membaca huruf hijaiyyah terkadang tidak memperhatikan kaidah bacaanmakhorijul huruf. Makhrorijul huruf adalah cara melafalkan atau tempatkeluarnya huruf hijaiyyah. Dengan adanya teknologi pengenalan suara, dalammelafalkan huruf hijaiyyah dapat dilihat perbedaannya secara kuantitatif melaluisistem. Terdapat dua tahapan agar suara dapat dikenali, dengan terlebih dahulumelakukan ekstraksi sinyal suara selanjutnya melakukan identifikasi

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He, Guanghui. "Safety state identification of concrete pumping pipeline based on multi-channel audio signals." E3S Web of Conferences 198 (2020): 01001. http://dx.doi.org/10.1051/e3sconf/202019801001.

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Анотація:

Based on the principle of superposition and attenuation of sound propagation, a Multi-point Multi-channel noise Reduction Method (MMRM) for audio monitoring is proposed. With the help of the proposed noise reduction method, the sounds made by the concrete pumping-pipelines themselves on the construction site and the environmental noises are separated in real time. Then, the Mel-Frequency Cepstral Coefficients (MFCCs) and spectral centroids of the filtered pumping-pipeline signals are extracted in real time as the features. Finally, the distance between the currently obtained feature vector of

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Koolagudi, Shashidhar G., Deepika Rastogi, and K. Sreenivasa Rao. "Identification of Language using Mel-Frequency Cepstral Coefficients (MFCC)." Procedia Engineering 38 (2012): 3391–98. http://dx.doi.org/10.1016/j.proeng.2012.06.392.

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Liu, Haitao, Yunfan Xu, Yuefeng Qi, Haosong Yang, and Weihong Bi. "Rapid Diagnosis of Distributed Acoustic Sensing Vibration Signals Using Mel-Frequency Cepstral Coefficients and Liquid Neural Networks." Sensors 25, no. 10 (2025): 3090. https://doi.org/10.3390/s25103090.

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Анотація:

Distributed Acoustic Sensing (DAS) systems face increasing challenges in massive data processing and real-time fault diagnosis due to the growing complexity of industrial environments and data volume. To address these issues, an end-to-end diagnostic framework is developed, integrating Mel-Frequency Cepstral Coefficients (MFCCs) for high-efficiency signal compression and Liquid Neural Networks (LNNs) for lightweight, real-time classification. The MFCC algorithm, originally used in speech processing, is adapted to extract key features from DAS vibration signals, achieving compression ratios of

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Metzner, Willian Velloso, and Gustavo Cesar Dacanal. "Monitoring Agitation Intensity in Fluidized Beds Containing Inert Particles via Acoustic Emissions and Neural Networks." Processes 12, no. 12 (2024): 2691. http://dx.doi.org/10.3390/pr12122691.

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This study utilized passive acoustic emissions from a fluidized bed containing spherical inert ABS particles, captured by an external piezoelectric microphone, to monitor fluidization agitation intensity. Acoustic signals were recorded during fluidization profiles achieved under air velocities ranging from 0.5 to 3.0 m/s and during the drying of water or maltodextrin aqueous solution (1:5 w/w) introduced as droplets. Analyzing audio features like waveforms, the Discrete Fourier Transform (DFT), and Mel Frequency Cepstral Coefficients (MFCCs) revealed changes corresponding to the agitation inte

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Urrutia, Robin, Diego Espejo, Natalia Evens, et al. "Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions." Sensors 23, no. 23 (2023): 9297. http://dx.doi.org/10.3390/s23239297.

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Анотація:

This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distribu

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Lee, Ji-Yeoun. "Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters." Applied Sciences 11, no. 21 (2021): 9836. http://dx.doi.org/10.3390/app11219836.

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Анотація:

The objective of this research was to develop deep learning classifiers and various parameters that provide an accurate and objective system for classifying elderly and young voice signals. This work focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for the detection of elderly voice signals using mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstrum coefficients (LPCCs), skewness, as well as kurtosis parameters. In total, 126 subjects (63 elderly and 63 young) were obtained from the Saarbruecken voice databa

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Elizarov, D. A., P. A. Ashaeva, and E. A. Stepanova. "Voice authentication module using mel-cepstral coefficients." Herald of Dagestan State Technical University. Technical Sciences 51, no. 2 (2024): 77–82. http://dx.doi.org/10.21822/2073-6185-2024-51-2-77-82.

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Анотація:

Objective. The purpose of the study is to develop and apply a method for extracting information about the identity of users from recordings of their voices using the calculation of mel-cepstral coefficients.Method. In the study of the application of methods for extracting informative features from a voice recording, allowing identification of the speaker, an authentication scheme using mel-cepstral coefficients is presented.Result. Based on this method, an authentication module was implemented using audio recordings of user voices using the simplest MFCC. The authentication module was develope

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Yan, Hao, Huajun Bai, Xianbiao Zhan, Zhenghao Wu, Liang Wen, and Xisheng Jia. "Combination of VMD Mapping MFCC and LSTM: A New Acoustic Fault Diagnosis Method of Diesel Engine." Sensors 22, no. 21 (2022): 8325. http://dx.doi.org/10.3390/s22218325.

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Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on variational modal decomposition mapping Mel frequency cepstral coefficients (MFCC) and long-short-term memory network is proposed. Variational mode decomposition (VMD) is used to remove noise from the original signal and differentiate the signal into multiple modes. The sound pressure signals of different modes are mapped to the Mel filter b

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Chen, Young-Long, Neng-Chung Wang, Jing-Fong Ciou, and Rui-Qi Lin. "Combined Bidirectional Long Short-Term Memory with Mel-Frequency Cepstral Coefficients Using Autoencoder for Speaker Recognition." Applied Sciences 13, no. 12 (2023): 7008. http://dx.doi.org/10.3390/app13127008.

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Анотація:

Recently, neural network technology has shown remarkable progress in speech recognition, including word classification, emotion recognition, and identity recognition. This paper introduces three novel speaker recognition methods to improve accuracy. The first method, called long short-term memory with mel-frequency cepstral coefficients for triplet loss (LSTM-MFCC-TL), utilizes MFCC as input features for the LSTM model and incorporates triplet loss and cluster training for effective training. The second method, bidirectional long short-term memory with mel-frequency cepstral coefficients for t

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Alluhaidan, Ala Saleh, Oumaima Saidani, Rashid Jahangir, Muhammad Asif Nauman, and Omnia Saidani Neffati. "Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network." Applied Sciences 13, no. 8 (2023): 4750. http://dx.doi.org/10.3390/app13084750.

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Анотація:

Speech emotion recognition (SER) is the process of predicting human emotions from audio signals using artificial intelligence (AI) techniques. SER technologies have a wide range of applications in areas such as psychology, medicine, education, and entertainment. Extracting relevant features from audio signals is a crucial task in the SER process to correctly identify emotions. Several studies on SER have employed short-time features such as Mel frequency cepstral coefficients (MFCCs), due to their efficiency in capturing the periodic nature of audio signals. However, these features are limited

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Vivek, C., M. Indu, and N. Nandhini. "Speech Recognition Using Artificial Neural Network." Journal of Cognitive Human-Computer Interaction 5, no. 2 (2023): 08–14. http://dx.doi.org/10.54216/jchci.050201.

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Анотація:

Speech is a verbal communication used by humans through language. Likewise speech recognition is a process of converting speech to text. This paper provides a study of use of artificial neural networks(ANN) in speech recognition. Hidden Markov models (HMM) is a traditional statistical techniques for performing speech recognition. In speech detection software, Mel frequency cepstral coefficients (MFCCs) are frequently used. With different approaches evolving, we deal with the features used to recognize the speech pattern and implementation of speech recognition in the efficient types of artific

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Anacleto Silva, Harry. "ATRIBUTOS PNCC PARA RECONOCIMIENTO ROBUSTO DE LOCUTOR INDEPENDIENTE DEL TEXTO." INGENIERÍA: Ciencia, Tecnología e Innovación 3, no. 2 (2016): 35–40. http://dx.doi.org/10.26495/icti.v3i2.431.

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El reconocimiento automático de locutores ha sido sujeto de intensa investigación durante toda la década pasada. Sin embargo las características, del estado de arte de los algoritmos son drásticamente degradados en presencia de ruido. Este artículo se centra en la aplicación de una nueva técnica llamada Power-Normalized Cepstral Coefficients (PNCC) para el reconocimiento de locutor independiente del texto. El objetivo de este estudio es evaluar las características de esta técnica en comparación con la técnica convencional Mel Frequency Cepstral Coefficients (MFCC) y la técnica Gammatone Freque

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Luz, Jederson S., Myllena C. De Oliveira, Fábia de M. Pereira, Flávio H. D. De Araújo, and Deborah M. V. Magalhães. "Cepstral and Deep Features for Apis mellifera Hive Strength Classification." Journal of Internet Services and Applications 15, no. 1 (2024): 548–60. https://doi.org/10.5753/jisa.2024.4015.

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Regular management practices are crucial to assessing colonies’ conditions and implementing measures to improve their strength. However, constant revisions can induce stress and even contribute to swarm loss. Therefore, effective management that considers the well-being of the bees is necessary. In order to assist the beekeeper in managing the hives, this study proposes a noninvasive approach integrating Apis mellifera L., 1758 (Hymenoptera: Apidae) colony sound processing with machine learning and deep learning techniques to identify colony strength, essential for the productivity of apicultu

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Muhammad, Ghulam, and Khalid Alghathbar. "Environment Recognition for Digital Audio Forensics Using MPEG-7 and MEL Cepstral Features." Journal of Electrical Engineering 62, no. 4 (2011): 199–205. http://dx.doi.org/10.2478/v10187-011-0032-0.

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Environment Recognition for Digital Audio Forensics Using MPEG-7 and MEL Cepstral FeaturesEnvironment recognition from digital audio for forensics application is a growing area of interest. However, compared to other branches of audio forensics, it is a less researched one. Especially less attention has been given to detect environment from files where foreground speech is present, which is a forensics scenario. In this paper, we perform several experiments focusing on the problems of environment recognition from audio particularly for forensics application. Experimental results show that the

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Kothuri, Jhansi. "Speech Emotion Recognition: An LSTM Approach." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45580.

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Abstract – This paper presents a novel approach to Speech Emotion Recognition (SER) utilizing a Long Short-Term Memory (LSTM) network to classify emotions from audio inputs in real-time. The primary goal of this research is to accurately identify various emotions, including happiness, sadness, anger, fear, and surprise, enhancing user experience in applications such as human-computer interaction, virtual assistants, and mental health monitoring. The methodology involves a comprehensive process that begins with the preprocessing of audio signals to ensure clarity and consistency. This is follow

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Mahalakshmi, P. "A REVIEW ON VOICE ACTIVITY DETECTION AND MEL-FREQUENCY CEPSTRAL COEFFICIENTS FOR SPEAKER RECOGNITION (TREND ANALYSIS)." Asian Journal of Pharmaceutical and Clinical Research 9, no. 9 (2016): 360. http://dx.doi.org/10.22159/ajpcr.2016.v9s3.14352.

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ABSTRACTObjective: The objective of this review article is to give a complete review of various techniques that are used for speech recognition purposes overtwo decades.Methods: VAD-Voice Activity Detection, SAD-Speech Activity Detection techniques are discussed that are used to distinguish voiced from unvoicedsignals and MFCC- Mel Frequency Cepstral Coefficient technique is discussed which detects specific features.Results: The review results show that research in MFCC has been dominant in signal processing in comparison to VAD and other existing techniques.Conclusion: A comparison of differe

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Ramashini, Murugaiya, P. Emeroylariffion Abas, Kusuma Mohanchandra, and Liyanage C. De Silva. "Robust cepstral feature for bird sound classification." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1477. http://dx.doi.org/10.11591/ijece.v12i2.pp1477-1487.

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Birds are excellent environmental indicators and may indicate sustainability of the ecosystem; birds may be used to provide provisioning, regulating, and supporting services. Therefore, birdlife conservation-related researches always receive centre stage. Due to the airborne nature of birds and the dense nature of the tropical forest, bird identifications through audio may be a better solution than visual identification. The goal of this study is to find the most appropriate cepstral features that can be used to classify bird sounds more accurately. Fifteen (15) endemic Bornean bird sounds hav

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Murugaiya, Ramashini, Emeroylariffion Abas Pg, Mohanchandra Kusuma, and C. De Silva Liyanage. "Robust cepstral feature for bird sound classification." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1477–87. https://doi.org/10.11591/ijece.v12i2.pp1477-1487.

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Анотація:

Birds are excellent environmental indicators and may indicate sustainability of the ecosystem; birds may be used to provide provisioning, regulating, and supporting services. Therefore, birdlife conservation-related researches always receive centre stage. Due to the airborne nature of birds and the dense nature of the tropical forest, bird identifications through audio may be a better solution than visual identification. The goal of this study is to find the most appropriate cepstral features that can be used to classify bird sounds more accurately. Fifteen (15) endemic Bornean bird sounds hav

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