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Journal articles on the topic 'MFCC (Mel Frequency Cepstral Coefficient)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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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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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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Heriyanto, Heriyanto, Sri Hartati, and Agfianto Eko Putra. "EKSTRAKSI CIRI MEL FREQUENCY CEPSTRAL COEFFICIENT (MFCC) DAN RERATA COEFFICIENT UNTUK PENGECEKAN BACAAN AL-QUR’AN." Telematika 15, no. 2 (2018): 99. http://dx.doi.org/10.31315/telematika.v15i2.3123.

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AbstrakBelajar membaca Al-Qur’an menggunakan alat bantu aplikasi sangat diperlukan dalam mempermudah dan memahami bacaan Al-Qur’an. Pengecekan bacaan Al-Qur’an salah satu metode dengan MFCC untuk pengenalan suara cukup baik dalam speech recognition.Metode tersebut telah lama diperkenalkan oleh Davis dan Mermelstein sekitar tahun 1980. MFCC merupakan metode ekstraksi ciri untuk mendapatkan cepstral coefficient dan frame sehingga dapat digunakan untuk pemrosesan pengenalan suara agar lebih baik dalam ketepatan. Tahapan MFCC mulai dari pre-emphasis, frame blocking, windowing, Fast Fourier Transfo

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Heriyanto, Heriyanto, Tenia Wahyuningrum, and Gita Fadila Fitriana. "Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features." JURNAL INFOTEL 13, no. 2 (2021): 84–93. http://dx.doi.org/10.20895/infotel.v13i2.657.

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This study investigates the sound of Hanacaraka in Javanese to select the best frame feature in checking the reading sound. Selection of the right frame feature is needed in speech recognition because certain frames have accuracy at their dominant weight, so it is necessary to match frames with the best accuracy. Common and widely used feature extraction models include the Mel Frequency Cepstral Coefficient (MFCC). The MFCC method has an accuracy of 50% to 60%. This research uses MFCC and the selection of Dominant Weight features for the Javanese language script sound Hanacaraka which produces

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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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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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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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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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Li, Guan Yu, Hong Zhi Yu, Yong Hong Li, and Ning Ma. "Features Extraction for Lhasa Tibetan Speech Recognition." Applied Mechanics and Materials 571-572 (June 2014): 205–8. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.205.

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Speech feature extraction is discussed. Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction coefficient (PLP) method is analyzed. These two types of features are extracted in Lhasa large vocabulary continuous speech recognition system. Then the recognition results are compared.

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Heriyanto, Heriyanto, Herlina Jayadianti, and Juwairiah Juwairiah. "The Implementation Of Mfcc Feature Extraction And Selection of Cepstral Coefficient for Qur’an Recitation in TPA (Qur’an Learning Center) Nurul Huda Plus Purbayan." RSF Conference Series: Engineering and Technology 1, no. 1 (2021): 453–78. http://dx.doi.org/10.31098/cset.v1i1.417.

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There are two approaches to Qur’an recitation, namely talaqqi and qira'ati. Both approaches use the science of recitation containing knowledge of the rules and procedures for reading the Qur'an properly. Talaqqi requires the teacher and students to sit facing each other while qira'ati is the recitation of the Qur'an with rhythms and tones. Many studies have developed an automatic speech recognition system for Qur’an recitation to help the learning process. Feature extraction model using Mel Frequency Cepstral Coefficient (MFCC) and Linear Predictive Code (LPC). The MFCC method has an accuracy

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Lakshmipriya, K., S. P. Charu Prafulla, S. Lokesh, and O. Uma Maheswari. "Malicious UAV Detection Using Blind Source Separation Algorithm and Neural Network Classifier." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–16. http://dx.doi.org/10.55041/ijsrem36797.

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Unmanned aerial vehicle(UAV) technology is the rapid growing technology in the field of monitoring for security purposes, pesticides spraying and various other applications. In the recent days, one of the major concerns is entering of malicious UAVs into the secured perimeter that might result in Drone-based cyberattacks. So, the detection of these malicious UAVs are crucial. In this work, an acoustic method of detecting malicious UAVs is proposed. The mixed form of the acoustic signals of two kinds of drones, namely, Fixed- wing and Multi- rotor are passed through the Blind Source Separation

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Helmiyah, Siti, Abdul Fadlil, and Anton Yudhana. "Pengenalan Pola Emosi Manusia Berdasarkan Ucapan Menggunakan Ekstraksi Fitur Mel-Frequency Cepstral Coefficients (MFCC)." CogITo Smart Journal 4, no. 2 (2019): 372. http://dx.doi.org/10.31154/cogito.v4i2.129.372-381.

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Human emotion recognition subject becomes important due to it's usability in daily lifestyle which requires human and computer interraction. Human emotion recognition is a complex problem due to the difference within custom tradition and specific dialect which exists on different ethnic, region and community. This problem also exacerbated due to objectivity assessment for the emotion is difficult since emotion happens unconsciously. This research conducts an experiment to discover pattern of emotion based on feature extracted from speech. Method used for feature extraction on this experiment i

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Heriyanto, Heriyanto, and Dyah Ayu Irawati. "Comparison of Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction, With and Without Framing Feature Selection, to Test the Shahada Recitation." RSF Conference Series: Engineering and Technology 1, no. 1 (2021): 335–54. http://dx.doi.org/10.31098/cset.v1i1.395.

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Voice research for feature extraction using MFCC. Introduction with feature extraction as the first step to get features. Features need to be done further through feature selection. The feature selection in this research used the Dominant Weight feature for the Shahada voice, which produced frames and cepstral coefficients as the feature extraction. The cepstral coefficient was used from 0 to 23 or 24 cepstral coefficients. At the same time, the taken frame consisted of 0 to 10 frames or eleven frames. Voting as many as 300 samples of recorded voices were tested on 200 voices of both male and

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Heriyanto, Heriyanto. "Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection." Telematika 18, no. 1 (2021): 88. http://dx.doi.org/10.31315/telematika.v18i1.4495.

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Purpose:Select the right features on the frame for good accuracyDesign/methodology/approach:Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:The accuracy results show that the MFCC method with the 9th frame selection has a higher accuracy rate of 85% compared to other frames.Originality/value/state of the art:Selection of the appropriate features on the frame.

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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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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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Bhalke, Daulappa Guranna, C. B. Rama Rao, and Dattatraya Bormane. "Hybridisation of Mel Frequency Cepstral Coefficient and Higher Order Spectral Features for Musical Instruments Classification." Archives of Acoustics 41, no. 3 (2016): 427–36. http://dx.doi.org/10.1515/aoa-2016-0042.

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Abstract This paper presents the classification of musical instruments using Mel Frequency Cepstral Coefficients (MFCC) and Higher Order Spectral features. MFCC, cepstral, temporal, spectral, and timbral features have been widely used in the task of musical instrument classification. As music sound signal is generated using non-linear dynamics, non-linearity and non-Gaussianity of the musical instruments are important features which have not been considered in the past. In this paper, hybridisation of MFCC and Higher Order Spectral (HOS) based features have been used in the task of musical ins

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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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Dirgantoro, Kevin Putra, Bambang Hidayat, and Nur Andini. "PERBANDINGAN STEGANALISIS SINYAL WICARA BERFORMAT .WAV ANTARA METODE ANALISIS CEPSTRAL DAN MEL-FREQUENCY CEPSTRAL COEFFICIENT (MFCC)." TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika 3, no. 2 (2019): 56. http://dx.doi.org/10.25124/tektrika.v3i2.2224.

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Teknik menyembunyikan pesan rahasia ke dalam suatu data tertentu atau yang biasa dikenal dengan steganografi mengalami perkembangan yang sangat pesat. Namun, ternyata metode penyembunyian pesan ini juga menimbulkan masalah, di antaranya pihak-pihak yang tidak bertanggung jawab menggunakan teknik tersebut untuk kegiatan kriminalitas. Oleh karena itu, diperlukan teknik untuk mendeteksi pesan tersembunyi di dalam suatu data. Teknik tersebut dikenal dengan istilah steganalisis. Pada penelitian ini, dilakukan analisis terhadap berkas sinyal wicara yang berformat .wav, dengan menggunakan dua metode,

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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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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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Sakshi, Gupta, S. Shukla Ravi, and K. Shukla Rajesh. "Weighted Mel frequency cepstral coefficient based feature extraction for automatic assessment of stuttered speech using Bi-directional LSTM." Indian Journal of Science and Technology 14, no. 5 (2021): 457–72. https://doi.org/10.17485/IJST/v14i5.2276.

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Abstract <strong>Objective:</strong> To propose a system for automatic assessment of stuttered speech to help the Speech Language Pathologists during their treatment of a person who stutters. <strong>Methods:</strong> A novel technique is proposed for automatic assessment of stuttered speech, composed of feature extraction based on Weighted Mel Frequency Cepstral Coefficient and classification using Bi-directional Long-Short Term Memory neural network. It mainly focuses on detecting prolongation and syllable, word, and phrase repetition in stuttered events.<strong> Findings

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Bhalke, Daulappa Guranna, Betsy Rajesh, and Dattatraya Shankar Bormane. "Automatic Genre Classification Using Fractional Fourier Transform Based Mel Frequency Cepstral Coefficient and Timbral Features." Archives of Acoustics 42, no. 2 (2017): 213–22. http://dx.doi.org/10.1515/aoa-2017-0024.

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Abstract This paper presents the Automatic Genre Classification of Indian Tamil Music and Western Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted from music excerpts has been analysed, to identify the appropriate feature descriptors for the two major genres of Indian Tamil music, namely Classical music (Carnatic based devotional hymn compos

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Rasyid, Muhammad Fahim, Herlina Jayadianti, and Herry Sofyan. "APLIKASI PENGENALAN PENUTUR PADA IDENTIFIKASI SUARA PENELEPON MENGGUNAKAN MEL-FREQUENCY CEPSTRAL COEFFICIENT DAN VECTOR QUANTIZATION (Studi Kasus : Layanan Hotline Universitas Pembangunan Nasional “Veteran” Yogyakarta)." Telematika 17, no. 2 (2020): 68. http://dx.doi.org/10.31315/telematika.v1i1.3380.

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Layanan hotline Universitas Pembangunan Nasional “Veteran” Yogyakarta merupakan layanan yang dapat digunakan oleh semua orang. Layanan tersebut digunakan dosen dan pegawai untuk berbagi informasi dengan bagian-bagian yang berlokasi di gedung rektorat. Penelepon dapat berkomunikasi dengan bagian yang dituju apabila telah teridentifikasi oleh petugas layanan hotline. Terminologi identitas yang terdiri dari nama, jabatan serta asal jurusan atau bagian ditanyakan saat proses identifikasi. Tidak terdapat catatan hasil identifikasi penelepon baik dalam bentuk fisik maupun basis data yang terekam pad

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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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Cinoglu, Bahadir, Umut Durak, and T. Hikmet Karakoc. "Utilizing Mel-Frequency Cepstral Coefficients for Acoustic Diagnostics of Damaged UAV Propellers." International Journal of Aviation Science and Technology vm05, is02 (2024): 79–89. http://dx.doi.org/10.23890/ijast.vm05is02.0201.

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In this study, the diagnostic potential of the acoustic signatures of Unmanned Aerial Vehicle (UAVs) propellers which is one of the critical components of these vehicles were examined under different damage conditions. For this purpose, a test bench was set up and acoustic data of five different damaged propellers and one undamaged propeller were collected. The methodology emphasized contains using an omnidirectional microphone to collect data under three different thrust levels which correspond to 25%, 50% and 75%. Propeller acoustics sound characteristics extracted using the Mel Frequency Ce

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Boubakeur, Khadidja Nesrine, and Mohamed Debyeche. "Formants and Prosodic Features' Effects on Arabic Speaker Identification Accuracy in Noisy Environments." AL-Lisaniyyat 30, no. 2 (2024): 40–52. https://doi.org/10.61850/allj.v30i2.734.

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This study investigates the use of formants and prosodic features, specifically pitch and intensity, for speaker identification in realconditions. To enhance the robustness of the acoustic models against speech signal variations in noisy environments, Mel-Frequency Cepstral Coefficient (MFCC) are added to these features. A Speaker Identification system based on Hidden Markov Models (HMM) is implemented in the independent text mode. The combination of formants and prosodic features with cepstral features improves the identification accuracy, particularly in high-noise environments, up to 10%, i

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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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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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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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Sarkar, Swagata, Sanjana R, Rajalakshmi S, and Harini T J. "Simulation and detection of tamil speech accent using modified mel frequency cepstral coefficient algorithm." International Journal of Engineering & Technology 7, no. 3.3 (2018): 426. http://dx.doi.org/10.14419/ijet.v7i2.33.14202.

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Automatic Speech reconstruction system is a topic of interest of many researchers. Since many online courses are come into the picture, so recent researchers are concentrating on speech accent recognition. Many works have been done in this field. In this paper speech accent recognition of Tamil speech from different zones of Tamilnadu is addressed. Hidden Markov Model (HMM) and Viterbi algorithms are very popularly used algorithms. Researchers have worked with Mel Frequency Cepstral Coefficients (MFCC) to identify speech as well as speech accent. In this paper speech accent features are identi

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Abbasi, Muhammad Daud, Zubair Sajid, Shahzad Karim Khawer, Syed Zain Mir, Abdul Basit, and Muhammad Kashif. "Automatic Speech Recognition by Using Neural Network Based on Mel Frequency Cepstral Coefficient." Asian Bulletin of Big Data Management 5, no. 2 (2025): 63–85. https://doi.org/10.62019/vs3esy64.

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This paper deliberated and estimated the Neural Networks Automatic Speech Recognition (ASR) system based on an isolated small vocabulary speaker-independent manual cropping technique, from the training stage to the recognition stage. Besides this, the paper also examines three distinct blocks of speech recognition, i.e., Speech Preprocessor, Feature Extractor, and a Recognizer. Speech preprocessing involves windowing, framing, Short Term and Zero Crossing threshold energy, and End Point Detection calculation. Mel Frequency Cepstral Coefficients (MFCC) are extracted to represent the speech sign

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Tran, Thi Thanh. "Analysis of Building the Music Feature Extraction Systems: A Review." Engineering and Technology Journal 9, no. 05 (2024): 4055–60. https://doi.org/10.5281/zenodo.11242886.

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Music genre classification is a basic method for sound processing in the field of music retrieval. The application of machine learning has become increasingly popular in automatically classifying music genres. Therefore, in recent years, many methods have been studied and developed to solve this problem. In this article, an overview on the process and some music feature extraction methods is presented. Here, the feature extraction method using Mel Frequency Cepstral Coefficients (MFCC) is discussed in detail. Some typical results in using Mel Frequency Cepstral Coefficients for improving accur

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Sasilo, Ababil Azies, Rizal Adi Saputra, and Ika Purwanti Ningrum. "Sistem Pengenalan Suara Dengan Metode Mel Frequency Cepstral Coefficients Dan Gaussian Mixture Model." Komputika : Jurnal Sistem Komputer 11, no. 2 (2022): 203–10. http://dx.doi.org/10.34010/komputika.v11i2.6655.

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ABSTRAK – Teknologi biometrik sedang menjadi tren teknologi dalam berbagai bidang kehidupan. Teknologi biometrik memanfaatkan bagian tubuh manusia sebagai alat ukur sistem yang memiliki keunikan disetiap individu. Suara merupakan bagian tubuh manusia yang memiliki keunikan dan cocok dijadikan sebagai alat ukur dalam sistem yang mengadopsi teknologi biometrik. Sistem pengenalan suara adalah salah satu penerapan teknologi biometrik yang fokus kepada suara manusia. Sistem pengenalan suara memerlukan metode ekstraksi fitur dan metode klasifikasi, salah satu metode ekstraksi fitur adalah MFCC. MFCC

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Hasan Asda, Tayseer Mohammed, and Teddy Surya Gunawan. "Development of Quran Reciter Identification System Using MFCC and Neural Network." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (2016): 168. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp168-175.

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Currently, the Quran is recited by so many reciters with different ways and voices. Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about the development of Quran reciter recognition and identification system based on Mel Frequency Cepstral Coefficient (MFCC) feature extraction and artificial neural network (ANN). From every speech, characteristics from the utterances will be extracted through neural network model. In this paper a database o

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Sari, Puspita Kartika, Karlisa Priandana, and Agus Buono. "Perbandingan Sistem Perhitungan Suara Tepuk Tangan dengan Metode Berbasis Frekuensi dan Metode Berbasis Amplitudo." Jurnal Ilmu Komputer dan Agri-Informatika 2, no. 1 (2013): 29. http://dx.doi.org/10.29244/jika.2.1.29-37.

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<p>Sistem penilaian berdasarkan suara tepuk tangan sering digunakan dalam acara perlombaan di Indonesia. Namun, penentuan pemenang dengan cara konvensional cenderung subjektif. Penelitian ini mengembangkan sistem penilaian otomatis berbasis komputer untuk menghitung jumlah orang bertepuk tangan dan menentukan pemenang dari perlombaan berdasarkan tepuk tangan. Penelitian ini membandingkan dua metode yang dapat diterapkan yaitu metode berbasis frekuensi dan metode berbasis amplitudo. Metode yang berbasis frekuensi mengimplementasikan Mel Frequency Cepstral Coefficient (MFCC) sebagai pengek

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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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Vinaya, Anindita Adikaputri, and Tiffani Febiola Aciandra. "MEL-FREQUENCY CEPSTRAL COEFFICIENTS (MFCC) FEATURE FOR PUMP ANOMALY DETECTION IN NOISY ENVIRONMENTS." Jurnal Rekayasa Mesin 15, no. 2 (2024): 1175–86. http://dx.doi.org/10.21776/jrm.v15i2.1815.

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The continuity of a production process is supported by the availability of good assets. One of the efforts to support asset availability is through asset maintenance. One of the important assets in the industry is the pump. To detect anomalous conditions in the pump, the sound of the engine can be used. However, noisy environmental conditions can change the characteristics of the sound produced. This can have an impact on errors in identifying the condition of the machine. In this study, Mel Frequency Cepstral Coefficients (MFCC) is used, because the characteristics of MFCC are very attached t

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Altayeb, Muneera, and Areen Arabiat. "Crack detection based on mel-frequency cepstral coefficients features using multiple classifiers." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 3332. http://dx.doi.org/10.11591/ijece.v14i3.pp3332-3341.

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Crack detection plays an essential role in evaluating the strength of structures. In recent years, the use of machine learning and deep learning techniques combined with computer vision has emerged to assess the strength of structures and detect cracks. This research aims to use machine learning (ML) to create a crack detection model based on a dataset consisting of 2432 images of different surfaces that were divided into two groups: 70% of the training dataset and 30% of the testing dataset. The Orange3 data mining tool was used to build a crack detection model, where the support vector machi

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Dua, Mohit, Rajesh Kumar Aggarwal, and Mantosh Biswas. "Discriminative Training Using Noise Robust Integrated Features and Refined HMM Modeling." Journal of Intelligent Systems 29, no. 1 (2018): 327–44. http://dx.doi.org/10.1515/jisys-2017-0618.

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Abstract The classical approach to build an automatic speech recognition (ASR) system uses different feature extraction methods at the front end and various parameter classification techniques at the back end. The Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) techniques are the conventional approaches used for many years for feature extraction, and the hidden Markov model (HMM) has been the most obvious selection for feature classification. However, the performance of MFCC-HMM and PLP-HMM-based ASR system degrades in real-time environments. The proposed work

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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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Maulana, Patriaji Ibrahim, Arik Aranta, Fitri Bimantoro, and I. Gede Andika. "KLASIFIKASI MOOD MUSIK BERDASARKAN MEL FREQUENCY CEPSTRAL COEFFICIENTS DENGAN BACKPROPAGATION NEURAL NETWORK." Jurnal RESISTOR (Rekayasa Sistem Komputer) 5, no. 1 (2022): 72–85. http://dx.doi.org/10.31598/jurnalresistor.v5i1.1089.

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In music industry, each music is grouped by type, including music genre, artist identification, instrument introduction, and mood. Then came a field of research called Music Information Retrieval (MIR) which is a field of science that retrieves and processes the metadata of music files to perform the grouping. This research is based on the uniqueness of music that has its own mood implied in it. By creating a Machine Learning model using Backpropagation Neural Network (BPNN) based on the Mel Frequency Cepstral Coefficients (MFCC) input feature, it will be able to classify types of music based

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Altayeb, Muneera, and Areen Arabiat. "Crack detection based on mel-frequency cepstral coefficients features using multiple classifiers." Crack detection based on mel-frequency cepstral coefficients features using multiple classifiers 14, no. 3 (2024): 3332–41. https://doi.org/10.11591/ijece.v14i3.pp3332-3341.

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Crack detection plays an essential role in evaluating the strength of structures. In recent years, the use of machine learning and deep learning techniques combined with computer vision has emerged to assess the strength of structures and detect cracks. This research aims to use machine learning (ML) to create a crack detection model based on a dataset consisting of 2432 images of different surfaces that were divided into two groups: 70% of the training dataset and 30% of the testing dataset. The Orange3 data mining tool was used to build a crack detection mo

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Almanfaluti, Istian Kriya, and Judi Prajetno Sugiono. "Identifikasi Pola Suara Pada Bahasa Jawa Meggunakan Mel Frequency Cepstral Coefficients (MFCC)." JURNAL MEDIA INFORMATIKA BUDIDARMA 4, no. 1 (2020): 22. http://dx.doi.org/10.30865/mib.v4i1.1793.

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Voice Recognition is a process of developing systems used between computer and human. The purpose of this study is to find out the sound pattern of a person based on the spoken Javanese language. This study used the Mel Frequency Cepstral Coefficients (MFCC) method to solve the problem of feature extraction from human voices. Tests were carried out on 4 users consisting of 2 women and 2 men, each saying 1 word "KUTHO", the word pronounced 5 times. The results of the testing are to get a sound pattern from the characteristics of 1 person with another person so that research using the MFCC metho

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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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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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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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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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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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Li, Feng, Chenxi Cui, and Yashi Hu. "Classification of Infant Crying Sounds Using SE-ResNet-Transformer." Sensors 24, no. 20 (2024): 6575. http://dx.doi.org/10.3390/s24206575.

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Recently, emotion analysis has played an important role in the field of artificial intelligence, particularly in the study of speech emotion analysis, which can help understand one of the most direct ways of human emotional communication—speech. This study focuses on the emotion analysis of infant crying. Within cries lies a variety of information, including hunger, pain, and discomfort. This paper proposes an improved classification model using ResNet and transformer. It utilizes modified Mel-frequency cepstral coefficient Mel-frequency cepstral coefficient (MFCC) features obtained through fe

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Vashkevich, M. I., D. S. Likhachov, and E. S. Azarov. "Voice Analysis and Classification System Based on Perturbation Parameters and Cepstral Presentation in Psychoacoustic Scales." Doklady BGUIR 20, no. 1 (2022): 73–82. http://dx.doi.org/10.35596/1729-7648-2022-20-1-73-82.

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The paper describes an approach to design a system for analyzing and classification of a voice signal based on perturbation parameters and cepstral representation. Two variants of the cepstral representation of the voice signal are considered: based on mel-frequency cepstral coefficients (MFCC) and based on bark-frequency cepstral coefficients (BFCC). The work used a generally accepted approach to calculating the MFCC based on the time-frequency analysis by the method of discrete Fourier transform (DFT) with summation of energy in subbands. This method approximates the frequency resolution of

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