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Journal articles on the topic 'MFCC'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Lankala, Srinija, and Dr M. Ramana Reddy. "Design and Implementation of Energy-Efficient Floating Point MFCC Extraction Architecture for Speech Recognition Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 1217–25. http://dx.doi.org/10.22214/ijraset.2022.46807.

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Abstract: This brief presents an energy-efficient architecture to extract mel-frequency cepstrum coefficients (MFCCs) for realtime speech recognition systems. Based on the algorithmic property of MFCC feature extraction, the architecture is designed with floating-point arithmetic units to cover a wide dynamic range with a small bit-width. Moreover, various operations required in the MFCC extraction are examined to optimize operational bit-width and lookup tables needed to compute nonlinear functions, such as trigonometric and logarithmic functions. In addition, the dataflow of MFCC extraction

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ANCA, DUMITRU, DINU AURELIA, IURESCU IOANA C., and TOADER STEFAN. "Investigation of polypyrrole/TiO2 and poypyrrole/WO3 nanocomposites as anode modifier in salt bridge microbial fuel cell using municipal wastewater." Romanian Reports in Physics 76, no. 1 (2024): 502. http://dx.doi.org/10.59277/romrepphys.2024.76.502.

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Developing nanocomposite materials based on conducting polymers (CPs) and metal-oxide nanoparticles, which combine redox electrochemistry of CPs with intrinsic properties of nano-scale semiconducting materials, may offer improved microbial fuel cells (MFCs) performances. Polypyrrole (PPY) based nanocomposites were synthesized by chemical oxidative polymerization method and were further used as an anode modifier in salt bridge MFCs. The PPY-based nanocomposites were characterized by X-ray diffraction, Fourier-Transform Infrared (FTIR) spectroscopy, and Scanning Electron Microscopy (SEM). The ma

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Chu, Yun Yun, Wei Hua Xiong, Wei Wei Shi, and Yu Liu. "The Extraction of Differential MFCC Based on EMD." Applied Mechanics and Materials 313-314 (March 2013): 1167–70. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.1167.

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Feature extraction is the key to the object recognition. How to obtain effective, reliable characteristic parameters from the limited measured data is a question of great importance in feature extraction. This paper presents a method based on Empirical Mode Decomposition (EMD) for the extraction of Mel Frequency Cepstrum Coefficients (MFCCs) and its first order difference from original speech signals that contain four kinds of emotions such as anger, happiness, surprise and natural for emotion recognition. And the experiments compare the recognition rate of MFCC, differential MFCC (Both of the

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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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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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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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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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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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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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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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Raychaudhuri, Aryama, Rudra Narayan Sahoo, and Manaswini Behera. "Application of clayware ceramic separator modified with silica in microbial fuel cell for bioelectricity generation during rice mill wastewater treatment." Water Science and Technology 84, no. 1 (2021): 66–76. http://dx.doi.org/10.2166/wst.2021.213.

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Abstract Ceramic separators have recently been investigated as low-cost, robust, and sustainable separators for application in microbial fuel cells (MFC). In the present study, an attempt was made to develop a low-cost MFC employing a clayware ceramic separator modified with silica. The properties of separators with varying silica content (10%–40% w/w) were evaluated in terms of oxygen and proton diffusion. The membrane containing 30% silica exhibited improved performance compared to the unmodified membrane. Two identical MFCs, fabricated using ceramic separators with 30% silica content (MFCS-

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A. Hassan, Tariq, and Salam A. Hussein. "Modulation and Energy Components for Speaker Recognition System." International Journal of Advances in Scientific Research and Engineering 10, no. 08 (2024): 18–26. http://dx.doi.org/10.31695/ijasre.2024.8.3.

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This paper presents a model use the modulation and energy components for speaker recognition application, that is mainly follows theshort-term scenario in speech signal processing, and also introduce a parameter combination that includes the instantaneous components and the energy parameters. This will describe the importance of short-term speech analysis in estimating the modulation parameters and the role of the instantaneous energy in estimating the speaker-dependent parameters. Simply, the short-term scenario is used to, first; avoid the silent and background noise speech portions that pre

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Huizen, Roy Rudolf, and Florentina Tatrin Kurniati. "Feature extraction with mel scale separation method on noise audio recordings." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 815. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp815-824.

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This paper focuses on improving the accuracy of noise audio recordings. High-quality audio recording, extraction using the mel frequency cepstral coefficients (MFCC) method produces high accuracy. While the low-quality is because of noise, the accuracy is low. Improved accuracy by investigating the effect of bandwidth on the mel scale. The proposed improvement uses the mel scale separation methods into two frequency channels (MFCC dual-channel). For the comparison method using the mel scale bandwidth without separation (MFCC single-channel). Feature analysis using k-mean clustering. The data u

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Huizen, Roy Rudolf, and Florentina Tatrin Kurniati. "Feature extraction with mel scale separation method on noise audio recordings." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 815–24. https://doi.org/10.11591/ijeecs.v24.i2.pp815-824.

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This paper focuses on improving the accuracy of noise audio recordings. High-quality audio recording, extraction using the mel frequency cepstral coefficients (MFCC) method produces high accuracy. While the low-quality is because of noise, the accuracy is low. Improved accuracy by investigating the effect of bandwidth on the mel scale. The proposed improvement uses the mel scale separation methods into two frequency channels (MFCC dualchannel). For the comparison method using the mel scale bandwidth without separation (MFCC single-channel). Feature analysis using k-mean clustering. The data us

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Zhou, Ping, Xiao Pan Li, Jie Li, and Xin Xing Jing. "Speech Emotion Recognition Based on Mixed MFCC." Applied Mechanics and Materials 249-250 (December 2012): 1252–58. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.1252.

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Due to MFCC characteristic parameter in speech recognition has low identification accuracy when signal is intermediate, high frequency signal, this paper put forward a improved algorithm of combining MFCC, Mid-MFCC and IMFCC, using increase or decrease component method to calculate the contribution that MFCC, Mid-MFCC and IMFCC each order cepstrum component was used in speech emotion recognition, extracting several order cepstrum component with highest contribution from three characteristic parameters and forming a new characteristic parameter. The experiment results show that under the same e

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Sharma, Samiksha, Anupam Shukla, and Pankaj Mishra. "Speech and Language Recognition using MFCC and DELTA-MFCC." International Journal of Engineering Trends and Technology 12, no. 9 (2014): 449–52. http://dx.doi.org/10.14445/22315381/ijett-v12p286.

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15

G., Rupali, and S. K. Bhatia. "Analysis of MFCC and Multitaper MFCC Feature Extraction Methods." International Journal of Computer Applications 131, no. 4 (2015): 7–10. http://dx.doi.org/10.5120/ijca2015906883.

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Qian, Su Xiang, Fu Xi Liu, and Jian Cao. "Design of Sound Recognition System Based on Modified Neural Network." Applied Mechanics and Materials 278-280 (January 2013): 1178–81. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1178.

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Sound recognition based on neural network is a technique that can put a resolution to exceeding artificial identification. Three kinds of neural network recognition models, adopting MFCC and difference MFCC, are discussed. According to six kinds of typical gunshots we design a kind of sound recognition system based on BP neural network optimized by PSO that uses MFCC and difference MFCC as a characteristic quantity to recognize sound signal. In the experiment PSO is used to optimize the network’s initial weights and threshold value. The experiment’s results show that BP neural network optimize

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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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Prayogi, Yanuar Risah. "Modifikasi Metode MFCC untuk Identifikasi Pembicara di Lingkungan Ber-Noise." JOINTECS (Journal of Information Technology and Computer Science) 4, no. 1 (2019): 13. http://dx.doi.org/10.31328/jointecs.v4i1.999.

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Beberapa metode ekstraksi fitur untuk sistem identifikasi pembicara memiliki kelemahan yaitu ketika dilingkungan berderau hasil akurasinya menurun. Metode ekstraksi fitur Mel-Frequency Cepstral Coefficient (MFCC) merupakan metode ekstraksi sinyal suara yang peka terhadap derau. Metode MFCC menghasilkan akurasi yang tinggi ketika dilingkungan yang bersih. Sebaliknya ketika di lingkungan yang berderau akurasi yang dihasilkan turun drastis. Penelitian ini mengusulkan metode ekstraksi fitur menggunakan MFCC digabung dengan algoritma deteksi endpoint. Algoritma deteksi endpoint memisahkan daerah sp

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Phapatanaburi, Khomdet, Wongsathon Pathonsuwan, Longbiao Wang, et al. "Whispered Speech Detection Using Glottal Flow-Based Features." Symmetry 14, no. 4 (2022): 777. http://dx.doi.org/10.3390/sym14040777.

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Recent studies have reported that the performance of Automatic Speech Recognition (ASR) technologies designed for normal speech notably deteriorates when it is evaluated by whispered speech. Therefore, the detection of whispered speech is useful in order to attenuate the mismatch between training and testing situations. This paper proposes two new Glottal Flow (GF)-based features, namely, GF-based Mel-Frequency Cepstral Coefficient (GF-MFCC) as a magnitude-based feature and GF-based relative phase (GF-RP) as a phase-based feature for whispered speech detection. The main contribution of the pro

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Xie, Tao, Xiaodong Zheng, and Yan Zhang. "Seismic facies analysis based on speech recognition feature parameters." GEOPHYSICS 82, no. 3 (2017): O23—O35. http://dx.doi.org/10.1190/geo2016-0121.1.

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Seismic facies analysis plays an important role in seismic stratigraphy. Seismic attributes have been widely applied to seismic facies analysis. One of the most important steps is to optimize the most sensitive attributes with regard to reservoir characteristics. Using different attribute combinations in multidimensional analyses will yield different solutions. Acoustic waves and seismic waves propagating in an elastic medium follow the same law of physics. The generation process of a speech signal based on the acoustic model is similar to the seismic data of the convolution model. We have dev

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Nirmal, Asmita, Deepak Jayaswal, and Pramod H. Kachare. "Statistically Significant Duration-Independent-based Noise-Robust Speaker Verification." International Journal of Mathematical, Engineering and Management Sciences 9, no. 1 (2024): 147–62. http://dx.doi.org/10.33889/ijmems.2024.9.1.008.

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A speaker verification system models individual speakers using different speech features to improve their robustness. However, redundant features degrade the system's performance. This paper presents Statistically Significant Duration-Independent Mel frequency Cepstral Coefficients (SSDI-MFCC) features with the Extreme Gradient Boost classifier for improving the noise-robustness of speaker models. Eight statistical descriptors are used to generate signal duration-independent features, and a statistically significant feature subset is obtained using a t-test. A redeveloped Librispeech database

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Diaz, Ricky Aurelius Nurtanto, Ni Luh Gede Pivin Suwirmayanti, and Komang Budiarta. "PERBANDINGAN KUALITAS PENGENALAN SUARA UNTUK EKSTRAKSI FITUR MENGGUNAKAN MFCC DAN SPECTRAL." Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika 6, no. 1 (2024): 58–63. http://dx.doi.org/10.53580/naratif.v6i1.281.

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Tahapan awal dalam pengenalan suara adalah tahap ekstraksi fitur, dimana penerapan metode sangatlah dapat berdampak signifikan terhadap kualitas pengenalan suara, sehingga perlu dilakukan pemilihan metode yang tepat. Metode ekstraksi fitur untuk pengenalan suara diantaranya Mel-Frequency Cepstral Coefficients (MFCC) dan representasi spektral. MFCC telah menjadi standar dalam berbagai aplikasi pengenalan suara karena kemampuannya dalam menangkap karakteristik penting dari suara manusia. Sementara itu, representasi spektral memiliki pendekatan yang lebih sederhana dengan hanya menganalisis ampli

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Türkay, Yavuz, and Zekiye Seyma Tamay. "Pistachio Classification Based on Acoustic Systems and Machine Learning." Elektronika ir Elektrotechnika 30, no. 5 (2024): 4–13. https://doi.org/10.5755/j02.eie.38221.

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An acoustic emission and machine learning based pistachio classification system has been developed. This system performs feature extraction using Mel frequency cepstral coefficients (MFCC) and classification using support vector machine (SVM). This study revealed that when closed-shelled pistachios hit a steel plate, they have different frequency components compared to open-shelled pistachios. The audio signals of the samples selected for the classification process were recorded using a high sensitivity carbon microphone and MATLAB Analog Input Recorder. These recorded sounds were processed by

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Zhang, Lanyue, Di Wu, Xue Han, and Zhongrui Zhu. "Feature Extraction of Underwater Target Signal Using Mel Frequency Cepstrum Coefficients Based on Acoustic Vector Sensor." Journal of Sensors 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7864213.

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Feature extraction method using Mel frequency cepstrum coefficients (MFCC) based on acoustic vector sensor is researched in the paper. Signals of pressure are simulated as well as particle velocity of underwater target, and the features of underwater target using MFCC are extracted to verify the feasibility of the method. The experiment of feature extraction of two kinds of underwater targets is carried out, and these underwater targets are classified and recognized by Backpropagation (BP) neural network using fusion of multi-information. Results of the research show that MFCC, first-order dif

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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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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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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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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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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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Singh, Satyanand, and E. G. Rajan. "Vector Quantization Approach for Speaker Recognition using MFCC and Inverted MFCC." International Journal of Computer Applications 17, no. 1 (2011): 1–7. http://dx.doi.org/10.5120/2188-2774.

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Saumard, Matthieu. "Enhancing Speech Emotions Recognition Using Multivariate Functional Data Analysis." Big Data and Cognitive Computing 7, no. 3 (2023): 146. http://dx.doi.org/10.3390/bdcc7030146.

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Speech Emotions Recognition (SER) has gained significant attention in the fields of human–computer interaction and speech processing. In this article, we present a novel approach to improve SER performance by interpreting the Mel Frequency Cepstral Coefficients (MFCC) as a multivariate functional data object, which accelerates learning while maintaining high accuracy. To treat MFCCs as functional data, we preprocess them as images and apply resizing techniques. By representing MFCCs as functional data, we leverage the temporal dynamics of speech, capturing essential emotional cues more effecti

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Dadula, Cristina P., and Elmer P. Dadios. "Fuzzy Logic System for Abnormal Audio Event Detection Using Mel Frequency Cepstral Coefficients." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (2017): 205–10. http://dx.doi.org/10.20965/jaciii.2017.p0205.

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This paper presents a fuzzy logic system for audio event detection using mel frequency cepstral coefficients (MFCC). Twelve MFCC of audio samples were analyzed. The range of values of MFCC were obtained including its histogram. These values were normalized so that its minimum and maximum values lie between 0 and 1. Rules were formulated based on the histogram to classify audio samples as normal, gunshot, or crowd panic. Five MFCC were chosen as input to the fuzzy logic system. The membership functions and rules of the fuzzy logic system are defined based on the normalized histograms of MFCC. T

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Tirta, Luhfita, Joan Santoso, and Endang Setyati. "Pengenalan Lirik Lagu Otomatis Pada Video Lagu Indonesia Menggunakan Hidden Markov Model Yang Dilengkapi Music Removal." Journal of Information System,Graphics, Hospitality and Technology 4, no. 2 (2022): 86–94. http://dx.doi.org/10.37823/insight.v4i2.225.

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Video sangat penting untuk membuat informasi berupa suara dalam video agar dapat dipahami oleh semua kalangan masyarakat, dan orang-orang yang memiliki masalah pendengaran yaitu dengan cara paling alami terletak pada penggunaan subtitle. Oleh karena itu, peneliti membuat pengenalan lirik lagu otomatis pada video lagu Indonesia menggunakan Hidden Markov Model yang dilengkapi music removal. Dalam pengenalan suara lebih akurat dilakukan dengan menggunakan model HMM yang dilengkapi oleh MFCC (kata yang cocok 81% dan WER 19%) dibandingkan dengan model LDA + MFCC (kata yang cocok 71% dan WER 29%) da

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Sharif, Afriandy, Opim Salim Sitompul, and Erna Budhiarti Nababan. "Analysis Of Variation In The Number Of MFCC Features In Contrast To LSTM In The Classification Of English Accent Sounds." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, no. 2 (2023): 587–601. http://dx.doi.org/10.31289/jite.v6i2.8566.

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Various studies have been carried out to classify English accents using traditional classifiers and modern classifiers. In general, research on voice classification and voice recognition that has been done previously uses the MFCC method as voice feature extraction. The stages in this study began with importing datasets, data preprocessing of datasets, then performing MFCC feature extraction, conducting model training, testing model accuracy and displaying a confusion matrix on model accuracy. After that, an analysis of the classification has been carried out. The overall results of the 10 tes

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Zhu, Qiang, Zhong Wang, Yunfeng Dou, and Jian Zhou. "Whispered Speech Conversion Based on the Inversion of Mel Frequency Cepstral Coefficient Features." Algorithms 15, no. 2 (2022): 68. http://dx.doi.org/10.3390/a15020068.

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A conversion method based on the inversion of Mel frequency cepstral coefficient (MFCC) features was proposed to convert whispered speech into normal speech. First, the MFCC features of whispered speech and normal speech were extracted and a matching relation between the MFCC feature parameters of whispered speech and normal speech was developed through the Gaussian mixture model (GMM). Then, the MFCC feature parameters of normal speech corresponding to whispered speech were obtained based on the GMM and, finally, whispered speech was converted into normal speech through the inversion of MFCC

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Seok-Woo Jang, Sang-Hong Lee. "Comparative Study on Performance of Patient Classification Using Heart Sound and Deep Learning." Journal of Information Systems Engineering and Management 10, no. 18s (2025): 556–61. https://doi.org/10.52783/jisem.v10i18s.2945.

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Speech processing is emerging as an important application area of digital signal processing. In this paper, we present a performance comparison evaluation for patient classification based on Mel Frequency Cepstrum Coefficient (MFCC) using deep learning in the field of speech recognition. We conduct research by heart sound data of patients and healthy people. Each MFCC feature and heart sound feature are extracted by imaging them. We extract only MFCC features and compare the performance. In addition, we perform wavelet transformation to solve the noise problem of dataand learn the extracted he

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Rajesh, Sangeetha, and Nalini N. J. "Recognition of Musical Instrument Using Deep Learning Techniques." International Journal of Information Retrieval Research 11, no. 4 (2021): 41–60. http://dx.doi.org/10.4018/ijirr.2021100103.

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The proposed work investigates the impact of Mel Frequency Cepstral Coefficients (MFCC), Chroma DCT Reduced Pitch (CRP), and Chroma Energy Normalized Statistics (CENS) for instrument recognition from monophonic instrumental music clips using deep learning techniques, Bidirectional Recurrent Neural Networks with Long Short-Term Memory (BRNN-LSTM), stacked autoencoders (SAE), and Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM). Initially, MFCC, CENS, and CRP features are extracted from instrumental music clips collected as a dataset from various online libraries. In this work, t

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Dua, Mohit, Rajesh Kumar Aggarwal, and Mantosh Biswas. "Optimizing Integrated Features for Hindi Automatic Speech Recognition System." Journal of Intelligent Systems 29, no. 1 (2018): 959–76. http://dx.doi.org/10.1515/jisys-2018-0057.

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Abstract An automatic speech recognition (ASR) system translates spoken words or utterances (isolated, connected, continuous, and spontaneous) into text format. State-of-the-art ASR systems mainly use Mel frequency (MF) cepstral coefficient (MFCC), perceptual linear prediction (PLP), and Gammatone frequency (GF) cepstral coefficient (GFCC) for extracting features in the training phase of the ASR system. Initially, the paper proposes a sequential combination of all three feature extraction methods, taking two at a time. Six combinations, MF-PLP, PLP-MFCC, MF-GFCC, GF-MFCC, GF-PLP, and PLP-GFCC,

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Boulmaiz, Amira, Djemil Messadeg, Noureddine Doghmane, and Abdelmalik Taleb-Ahmed. "Design and Implementation of a Robust Acoustic Recognition System for Waterbird Species using TMS320C6713 DSK." International Journal of Ambient Computing and Intelligence 8, no. 1 (2017): 98–118. http://dx.doi.org/10.4018/ijaci.2017010105.

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In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water bird's sound, a tonal region detector (TRD) using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction (MFCC-SS) were extracted for clas

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Yang, Xing Hai, Wen Jie Fu, Yu Tai Wang, Jia Ding, and Chang Zhi Wei. "Heart Sound Clustering Based on Supervised Kohonen Network." Applied Mechanics and Materials 138-139 (November 2011): 1115–20. http://dx.doi.org/10.4028/www.scientific.net/amm.138-139.1115.

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In this paper, a new method based on Supervised Kohonen network (SKN) and Mel-frequency cepstrum coefficients (MFCC) is introduced. MFCC of heart sound signal are extracted firstly, and then features are got by calculating every order of MFCC average energy. Finally, SKN is used to identify heart sound. The experimental result shows that this algorithm has a good performance in heart sound clustering, and is of significant practical value.

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Rahman, Firdaus Noorhadi, Tri Listyorini, and Endang Supriyati. "ANALISIS AKURASI CNN PADA DATA OLAH SUARA MANUSIA MENGGUNAKAN PARAMETER KOEFISIEN MFCC DAN MAX LENGTH." Jurnal Digit 15, no. 1 (2025): 1. https://doi.org/10.51920/jd.v15i1.416.

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Di era digital, pengenalan suara manusia semakin berkembang sebagai salah satu solusi inovatif. Suara dapat digunakan untuk mempermudah dalam berbagai bidang. Penelitian ini bertujuan untuk menganalisis akurasi CNN pada data olah menggunakan parameter koefisien MFCC dan Max Length dalam mengklasifikasikan suara manusia berdasarkan delapan kelas suara yang mencakup tujuh kelas suara orang dan satu kelas suara bebas. Data suara menggunakan data primer dalam format WAV, kemudian diproses melalui dua tahapan preprocessing. Tahap pemrosesan ekstraksi fitur, menggunakan MFCC dengan parameter koefisi

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Panda, Siva Prasad, Rajsekhar Reddy A, and Uttam Prasad Panigrahy. "EVALUATION OF ANTICANCER ACTIVITY OF CUCUMIS CALLOSUS AGAINST EHRLICH’S ASCITES CARCINOMA BEARING MICE." Asian Journal of Pharmaceutical and Clinical Research 11, no. 10 (2018): 438. http://dx.doi.org/10.22159/ajpcr.2018.v11i10.27439.

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Objective: Our previous research isolated Cucurbitacin B (CuB) and ebenone leucopentaacetate (ELP) from methanolic fruit extract of Cucumis callosus (MFCC). The fruits of C. callosus (Rottl.) Cogn. (Family: Cucurbitaceae) plant have been traditionally used for antioxidant, anti-inflammatory, and antidiabetic actions. The objective of this research was to evaluate in vitro and in vivo anticancer effect of MFCC on Ehrlich Ascites Carcinoma (EAC) cell lines.Methods: In vitro anticancer assay of MFCC and standard drug, 5-fluorouracil (5-FU) was evaluated using Trypan blue and 3-(4, 5-dimethylthiaz

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Gao, Lun, Tai Fu Li, and Feng Wen. "Application in Extraction of Voice Recognition Characteristic for Relief Algorithm." Advanced Materials Research 765-767 (September 2013): 2772–75. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2772.

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Pertaining to the study on voice recognition issue, the characteristic selection study on voice signal is described via Relief Algorithm in this paper, it is started from 24-dimensional MFCC parameters to find out the most important MFCC parameters in the voice signal, under the condition that the recognition ratio doesnt change greatly, the optimized combination is performed for 24-dimensional MFCC parameters to provide a new orientation for characteristic of voice recognition.

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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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Gupta, Shikha, Jafreezal Jaafar, Wan Fatimah wan Ahmad, and Arpit Bansal. "Feature Extraction Using Mfcc." Signal & Image Processing : An International Journal 4, no. 4 (2013): 101–8. http://dx.doi.org/10.5121/sipij.2013.4408.

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Ruan, Peilin, Xu Zheng, Yi Qiu, and Zhiyong Hao. "A Binaural MFCC-CNN Sound Quality Model of High-Speed Train." Applied Sciences 12, no. 23 (2022): 12151. http://dx.doi.org/10.3390/app122312151.

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The high-speed train (HST) is one of the most important transport tools in China, and the sound quality of its interior noise affects passengers’ comfort. This paper proposes a HST sound quality model. The model combines Mel-scale frequency cepstral coefficients (MFCCs), the most popular spectral-based input parameter in deep learning models, with convolutional neural networks (CNNs) to evaluate the sound quality of HSTs. Meanwhile, two input channels are applied to simulate binaural hearing so that the different sound signals can be processed separately. The binaural MFCC-CNN model achieves a

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Fergyanto, E. Gunawan, and Idananta Kanyadian. "Predicting the Level of Emotion by Means of Indonesian Speech Signal." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 2 (2017): 665–70. https://doi.org/10.12928/TELKOMNIKA.v15i2.3965.

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Understanding human emotion is of importance for developing better and facilitating smooth interpersonal relations. It becomes much more important because human thinking process and behavior are strongly influenced by the emotion. Align with these needs, an expert system that capable of predicting the emotion state would be useful for many practical applications. Based on a speech signal, the system has been widely developed for various languages. This study intends to evaluate to which extent MelFrequency Cepstral Coefficients (MFCC) features, besides Teager energy feature, derived from Indon

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Jokić, Ivan, Vlado Delić, and Zoran Perić. "APPLICATION OF MEL-FREQUENCY CEPSTRAL COEFFICIENTS IN AUTOMATIC SPEAKER RECOGNITION AS PART OF IOT SOLUTIONS FOR SECURITY AND OPTIMIZATION IN SMART CITIES." AlfaTech 1, no. 1 (2025): 5–10. https://doi.org/10.46793/alfatech1.1.05j.

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This paper presents an implementation of automatic speaker recognition utilizing feature vectors composed of 21 mel-frequency cepstral coefficients (MFCCs) as part of an IoT-driven solution for enhancing security and optimization in smart cities. Experiments are conducted on the Solo portion of the CHAINS database, containing 33 unique sentences pronounced by each of 36 speakers. Results indicate that recognition accuracy varies with the training and testing datasets and improves with longer test recordings. A comparative analysis of MFCC calculation methods reveals that accuracy is generally

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Sundar, D. Shyam, G. Kulaeep Nayyar, E. Barath Kumar, and Ms Pooja Kulkarni. "Classification and Recognition of Lung Sounds Based on BI-ResNet Model." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–5. https://doi.org/10.55041/ijsrem.ncft026.

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This research introduces a novel method for identifying lung sounds using a combination of Mel- Frequency Cepstral Coefficients (MFCC), Chroma features, and neural networks. Lung auscultation plays a crucial role in diagnosing respiratory illnesses, yet it typically depends on a clinician’s skill to detect and interpret subtle audio cues. To support and enhance this diagnostic process, we developed an automated system capable of accurately recognizing and categorizing lung sounds. MFCCs were utilized to analyze the spectral characteristics of audio signals, mimicking how the human ear processe

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Nichting, Thomas J., Maretha Bester, Rohan Joshi, et al. "Evidence and clinical relevance of maternal-fetal cardiac coupling: A scoping review." PLOS ONE 18, no. 7 (2023): e0287245. http://dx.doi.org/10.1371/journal.pone.0287245.

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Background Researchers have long suspected a mutual interaction between maternal and fetal heart rhythms, referred to as maternal-fetal cardiac coupling (MFCC). While several studies have been published on this phenomenon, they vary in terms of methodologies, populations assessed, and definitions of coupling. Moreover, a clear discussion of the potential clinical implications is often lacking. Subsequently, we perform a scoping review to map the current state of the research in this field and, by doing so, form a foundation for future clinically oriented research on this topic. Methods A liter

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Hidayat, Syahroni. "SPEECH RECOGNITION OF KV-PATTERNED INDONESIAN SYLLABLE USING MFCC, WAVELET AND HMM." Kursor 8, no. 2 (2016): 67. http://dx.doi.org/10.28961/kursor.v8i2.63.

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The Indonesian language is an agglutinative language which has complex suffixes and affixes attached on its root. For this reason there is a high possibility to recognize Indonesian speech based on its syllables. The syllable-based Indonesian speech recognition could reduce the database and recognize new Indonesian vocabularies which evolve as the result of language development. MFCC and WPT daubechies 3rd (DB3) and 7th (DB7) order methods are used in feature extraction process and HMM with Euclidean distance probability is applied for classification. The results shows that the best recognitio

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