Log in

Relevant bibliographies by topics / Mel-frequency Cepstrum Coefficient (MFCC) / Journal articles

To see the other types of publications on this topic, follow the link: Mel-frequency Cepstrum Coefficient (MFCC).

Journal articles on the topic 'Mel-frequency Cepstrum Coefficient (MFCC)'

Author: Grafiati

Published: 10 December 2022

Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Mel-frequency Cepstrum Coefficient (MFCC).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Mahalakshmi, P., Muruganandam M, and Sharmila A. "VOICE RECOGNITION SECURITY SYSTEM USING MEL-FREQUENCY CEPSTRUM COEFFICIENTS." Asian Journal of Pharmaceutical and Clinical Research 9, no. 9 (2016): 131. http://dx.doi.org/10.22159/ajpcr.2016.v9s3.13633.

Full text

Abstract:

ABSTRACTObjective: Voice Recognition is a fascinating field spanning several areas of computer science and mathematics. Reliable speaker recognition is a hardproblem, requiring a combination of many techniques; however modern methods have been able to achieve an impressive degree of accuracy. Theobjective of this work is to examine various speech and speaker recognition techniques and to apply them to build a simple voice recognition system.Method: The project is implemented on software which uses different techniques such as Mel frequency Cepstrum Coefficient (MFCC), VectorQuantization (VQ) w

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

5

He, Ai Xiang, Meng Ke Zhang, and Chen Chen Wang. "Coal Gangue Interface Recognition Based on MFCC Research." Applied Mechanics and Materials 411-414 (September 2013): 1058–61. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1058.

Full text

Abstract:

In view of the existing coal gangue interface identification technology use ray method that it is not applicable to the working face what it dose not contain radioactive elements in the roof or contain low amounts of radioactive elements, and the detection range of radar detection is lesser, the signal attenuation is relatively serious, so putting forward a kind of coal gangue interface recognition based on Mel frequency cepstrum coefficient of MFCC. The method using coal gangue were put down in the process of coal gangue on the difference of the characteristics of the acoustic signal recognit

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

Zhang, Hongxing, Hu Li, Wenxin Chen, and Hongjun Han. "Feature Extraction of Speech Signal Based on MFCC (Mel cepstrum coefficient)." Journal of Physics: Conference Series 2584, no. 1 (2023): 012143. http://dx.doi.org/10.1088/1742-6596/2584/1/012143.

Full text

Abstract:

Abstract Smart power plant is to establish a modern energy power system to achieve safe, efficient, green, and low-carbon power generation. Its characteristics are that the production process can be independently optimized, the relevant systems can collect, analyze, judge, and plan their own behavior, and intelligently and dynamically optimize equipment configuration and its parameters. This paper focuses on the optimal recognition state of MFCC in smart power plants. In this paper, we propose that by changing the number of filters and the order of MFCC to view the expression effect of the fin

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

de Souza, Edson Florentino, Túlio Nogueira Bittencourt, Diogo Ribeiro, and Hermes Carvalho. "Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges." Sustainability 14, no. 20 (2022): 13290. http://dx.doi.org/10.3390/su142013290.

Full text

Abstract:

In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) to extract the Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates the train, ballasted track and bridge behavior is presented. The formulation includes track irregularities and a damaged condition induced in a specified structure region. The feasibility of applying cepstrum analysis components to the indirect damage detection in HSR by on-board sensors is evaluated by numerical simulations, in

APA, Harvard, Vancouver, ISO, and other styles

11

Chen, Qianru, Zhifeng Wu, Qinghua Zhong, and Zhiwei Li. "Heart Sound Classification Based on Mel-Frequency Cepstrum Coefficient Features and Multi-Scale Residual Recurrent Neural Networks." Journal of Nanoelectronics and Optoelectronics 17, no. 8 (2022): 1144–53. http://dx.doi.org/10.1166/jno.2022.3305.

Full text

Abstract:

A rapid and accurate algorithm model of extracting heart sounds plays a vital role in the early detection of cardiovascular disorders, especially for small primary health care clinics. This paper proposes a heart sound extraction and classification algorithm based on static and dynamic combination of Mel-frequency cepstrum coefficient (MFCC) feature extraction and the multi-scale residual recurrent neural network (MsRes-RNN) algorithm model. The standard MFCC parameters represent the static characteristics of the signal. In contrast, the first-order and second-order MFCC parameters represent t

APA, Harvard, Vancouver, ISO, and other styles

12

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

Full text

Abstract:

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spec

APA, Harvard, Vancouver, ISO, and other styles

13

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

14

Sasongko, Sudi Mariyanto Al, Shofian Tsaury, Suthami Ariessaputra, and Syafaruddin Ch. "Mel Frequency Cepstral Coefficients (MFCC) Method and Multiple Adaline Neural Network Model for Speaker Identification." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2306. http://dx.doi.org/10.62527/joiv.7.4.1376.

Full text

Abstract:

Speech recognition technology makes human contact with the computer more accessible. There are two phases in the speaker recognition process: capturing or extracting voice features and identifying the speaker's voice pattern based on the voice characteristics of each speaker. Speakers consist of men and women. Their voices are recorded and stored in a computer database. Mel Frequency Cepstrum Coefficients (MFCC) are used at the voice extraction stage with a characteristic coefficient of 13. MFCC is based on variations in the response of the human ear's critical range to frequencies (linear and

APA, Harvard, Vancouver, ISO, and other styles

15

Sasongko, Sudi Mariyanto Al, Shofian Tsaury, Suthami Ariessaputra, and Syafaruddin Ch. "Mel Frequency Cepstral Coefficients (MFCC) Method and Multiple Adaline Neural Network Model for Speaker Identification." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2306. http://dx.doi.org/10.30630/joiv.7.4.01376.

Full text

Abstract:

Speech recognition technology makes human contact with the computer more accessible. There are two phases in the speaker recognition process: capturing or extracting voice features and identifying the speaker's voice pattern based on the voice characteristics of each speaker. Speakers consist of men and women. Their voices are recorded and stored in a computer database. Mel Frequency Cepstrum Coefficients (MFCC) are used at the voice extraction stage with a characteristic coefficient of 13. MFCC is based on variations in the response of the human ear's critical range to frequencies (linear and

APA, Harvard, Vancouver, ISO, and other styles

16

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

17

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

18

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

19

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

20

Ramadhina, Dea Sifana, Rita Magdalena, and Sofia Saidah. "Individual Identification Through Voice Using Mel-Frequency Cepstrum Coefficient (MFCC) and Hidden Markov Models (HMM) Method." Journal of Measurements, Electronics, Communications, and Systems 7, no. 1 (2020): 26. http://dx.doi.org/10.25124/jmecs.v7i1.3553.

Full text

Abstract:

Voice is one of the parameters in the identification process of a person. Through the voice, information will be obtained such as gender, age, and even the identity of the speaker. Speaker recognition is a method to narrow down crimes and frauds committed by voice. So that it will minimize the occurrence of faking one's identity. The Method of Mel Frequency Cepstrum Coefficient (MFCC) can be used in the speech recognition system. The process of feature extraction of speech signal using MFCC will produce acoustic speech signal. The classification, Hidden Markov Models (HMM) is used to match uni

APA, Harvard, Vancouver, ISO, and other styles

21

Hu, Wen-long, Shun-shan Feng, Bo Zhang, Yue-guang Gao, Xiang Xiao, and Qi-Huang. "Hybrid feature extraction method of MFCC+GFCC helicopter noise based on wavelet decomposition." Journal of Physics: Conference Series 2478, no. 12 (2023): 122008. http://dx.doi.org/10.1088/1742-6596/2478/12/122008.

Full text

Abstract:

Abstract Aiming at the issue that the recognition accuracy of traditional acoustic signal features is low for helicopter acoustic signals with wind noise in the near field, a method of extracting mixed noise features of MFCC+GFCC based on wavelet decomposition is proposed. Firstly, the three-layer wavelet decomposition and reconstruction are applied to the helicopter acoustic signals; then, the Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone-Frequency Cepstrum Coefficient (GFCC) are respectively extracted for the approximation and detail components; next, the coefficients of detail co

APA, Harvard, Vancouver, ISO, and other styles

22

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

23

Gao, Mei Juan, and Zhi Xin Yang. "Research and Realization on the Voice Command Recognition System for Robot Control Based on ARM9." Applied Mechanics and Materials 44-47 (December 2010): 1422–26. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1422.

Full text

Abstract:

In this paper, based on the study of two speech recognition algorithms, two designs of speech recognition system are given to realize this isolated speech recognition mobile robot control system based on ARM9 processor. The speech recognition process includes pretreatment of speech signal, characteristic extrication, pattern matching and post-processing. Mel-Frequency cepstrum coefficients (MFCC) and linear prediction cepstrum coefficients (LPCC) are the two most common parameters. Through analysis and comparison the parameters, MFCC shows more noise immunity than LPCC, so MFCC is selected as

APA, Harvard, Vancouver, ISO, and other styles

24

Mengistu, Abrham Debasu, and Dagnachew Melesew Alemayehu. "Text Independent Amharic Language Speaker Identification in Noisy Environments using Speech Processing Techniques." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (2017): 109. http://dx.doi.org/10.11591/ijeecs.v5.i1.pp109-114.

Full text

Abstract:

<p>In Ethiopia, the largest ethnic and linguistic groups are the Oromos, Amharas and Tigrayans. This paper presents the performance analysis of text-independent speaker identification system for the Amharic language in noisy environments. VQ (Vector Quantization), GMM (Gaussian Mixture Models), BPNN (Back propagation neural network), MFCC (Mel-frequency cepstrum coefficients), GFCC (Gammatone Frequency Cepstral Coefficients), and a hybrid approach had been use as techniques for identifying speakers of Amharic language in noisy environments. For the identification process, speech signals

APA, Harvard, Vancouver, ISO, and other styles

25

Punggawa Arcapada, Robbani, Widyadi Setiawan, and I. Made Arsa Suyadnya. "RANCANG BANGUN MODEL PENGIDENTIFIKASI SUARA HURUF HIJAIYAH DENGAN METODE MEL FREQUENCY CEPSTRUM COEFFICIENT DAN CONVOLUTIONAL NEURAL NETWORK." Jurnal SPEKTRUM 8, no. 4 (2022): 1. http://dx.doi.org/10.24843/spektrum.2021.v08.i04.p1.

Full text

Abstract:

Learning the Qur'an is a necessity for a Muslim, because the Qur'an acts as a guideand way of life. One thing that is learned in the Qur'an is how to pronounce Hijaiyah letters orMakharijul letters. In learning Makharijul Letters it takes an ustaz or accompanying teacher whois limited by distance and time. To overcome the limitations of distance and time, a learningapplication model is needed that can be accessed without the limitations of distance and thetime. This is studied aim to develop the Hijaiyah letter recognition model using the MelFrequency Cepstrum Coefficient (MFCC) and Convulatio

APA, Harvard, Vancouver, ISO, and other styles

26

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

27

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

28

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

29

Nursholihatun, Erina, Sudi Mariyanto Sasongko, and Abdullah Zainuddin. "IDENTIFIKASI SUARA MENGGUNAKAN METODE MEL FREQUENCY CEPSTRUM COEFFICIENTS (MFCC) DAN JARINGAN SYARAF TIRUAN BACKPROPAGATION." DIELEKTRIKA 7, no. 1 (2020): 48. http://dx.doi.org/10.29303/dielektrika.v7i1.232.

Full text

Abstract:

The voice is basic humans tool of communications. Speakers identifications is the process of recoqnizing the identity of a speaker by comparing the inputed voice features with all the features of each speaker in the database.There are two step of speaker identification process: feature extraction and pattern recognition. For the characteristic extraction phase using Mel Frequency Cepstrum Coefficient (MFCC) method. The method of pattern recognition using backpropagation artificial neural networks that compares the test data with the reference data in the database based on the variable result i

APA, Harvard, Vancouver, ISO, and other styles

30

Zhou, Ping, Jing Jing Ke, Xin Xing Jing, and Zhao Guo Cui. "Study on Characteristic Parameters of Speech Anti-Deliberate Imitation System." Applied Mechanics and Materials 475-476 (December 2013): 388–93. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.388.

Full text

Abstract:

Deliberate imitation which is the reproduction of another speakers voice and speech behavior can pose a threat to the security of the voice authentication system. Therefore effective characteristic parameters are the key to the anti-deliberate imitation. The study chose speech database of anti-deliberate imitation and investigated some common feature parameters separating capacity and descriptive power against voice deliberate imitation. The study compared the ranking of subjective evaluation and feature parameters Euclidean distance of imitators. The comparison results indicate that Mel frequ

APA, Harvard, Vancouver, ISO, and other styles

31

Rokanatnam, Thurgeaswary, and Hazinah Kutty Mammi. "Study on Gender Identification Based on Audio Recordings Using Gaussian Mixture Model and Mel Frequency Cepstrum Coefficient Technique." International Journal of Innovative Computing 11, no. 2 (2021): 35–41. http://dx.doi.org/10.11113/ijic.v11n2.343.

Full text

Abstract:

Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. The purpose of this study is to identify gender of speakers based on audio recordings. The objective of this study is to evaluate the accuracy rate of this technique to differentiate the gender and also to determine the performance rate to classify even when using self-acquired recordings. Audio forensics uses voice recordings as part of evidence to solve cases. This study is mainly conducted to provide an easier technique to identify the unknown speaker characteristics in forensic field. This e

APA, Harvard, Vancouver, ISO, and other styles

32

A, Prof Swethashree. "Speech Emotion Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2637–40. http://dx.doi.org/10.22214/ijraset.2021.37375.

Full text

Abstract:

Abstract: Speech Emotion Recognition, abbreviated as SER, the act of trying to identify a person's feelings and relationships. Affected situations from speech. This is because the truth often reflects the basic feelings of tone and tone of voice. Emotional awareness is a fast-growing field of research in recent years. Unlike humans, machines do not have the power to comprehend and express emotions. But human communication with the computer can be improved by using automatic sensory recognition, accordingly reducing the need for human intervention. In this project, basic emotions such as peace,

APA, Harvard, Vancouver, ISO, and other styles

33

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

34

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

35

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

36

Mustafa, Sahib Shareef, Abd Thulfiqar, and S. Mezaal Yaqeen. "Gender voice classification with huge accuracy rate." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 5 (2020): 2612~2617. https://doi.org/10.12928/TELKOMNIKA.v18i5.13717.

Full text

Abstract:

Gender voice recognition stands for an imperative research field in acoustics and speech processing as human voice shows very remarkable aspects. This study investigates speech signals to devise a gender classifier by speech analysis to forecast the gender of the speaker by investigating diverse parameters of the voice sample. A database has 2270 voice samples of celebrities, both male and female. Through Mel frequency cepstrum coefficient (MFCC), vector quantization (VQ), and machine learning algorithm (J 48), an accuracy of about 100% is achieved by the proposed classification technique base

APA, Harvard, Vancouver, ISO, and other styles

37

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

38

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

39

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

40

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

41

Hendry, Jans, Aditya Rachman, and Dodi Zulherman. "Recites fidelity detection system of al-Kautsar verse based on words using mel frequency cepstrum coefficients and cosine similarity." Jurnal Teknologi dan Sistem Komputer 8, no. 1 (2019): 27–35. http://dx.doi.org/10.14710/jtsiskom.8.1.2020.27-35.

Full text

Abstract:

In this study, a system has been developed to help detect the accuracy of the reading of the Koran in the Surah Al-Kautsar based on the accuracy of the number and pronunciation of words in one complete surah. This system is very dependent on the accuracy of word segmentation based on envelope signals. The feature extraction method used was Mel Frequency Cepstrum Coefficients (MFCC), while the Cosine Similarity method was used to detect the accuracy of the reading. From 60 data, 30 data were used for training, while the rest were for testing. From each of the 30 training and test data, 15 data

APA, Harvard, Vancouver, ISO, and other styles

42

Li, Xin Guang, Su Mei Li, Li Rui Jiang, and Sheng Bin Zhang. "Study of English Pronunciation Quality Evaluation System with Tone and Emotion Analysis Capabilities." Applied Mechanics and Materials 475-476 (December 2013): 318–23. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.318.

Full text

Abstract:

During the study of English sentence pronunciation evaluation system, we found that sentence pronunciation emotion and intonation evaluation are very important. Probabilistic neural network has been used to study English sentence pronunciation emotion, and DTW (Dynamic Time Warping) algorithm has been used in the intonation analysis. The probability neural network basic principle is introduced in this paper. An emotion recognition algorithm based on MFCC（Mel Frequency Cepstrum Coefficient）is present. The keynote and energy of the sentences are used to analyse the accuracy of the tones. The exp

APA, Harvard, Vancouver, ISO, and other styles

43

Nagaraja, B. G., and H. S. Jayanna. "Multilingual Speaker Identification by Combining Evidence from LPR and Multitaper MFCC." Journal of Intelligent Systems 22, no. 3 (2013): 241–51. http://dx.doi.org/10.1515/jisys-2013-0038.

Full text

Abstract:

AbstractIn this work, the significance of combining the evidence from multitaper mel-frequency cepstral coefficients (MFCC), linear prediction residual (LPR), and linear prediction residual phase (LPRP) features for multilingual speaker identification with the constraint of limited data condition is demonstrated. The LPR is derived from linear prediction analysis, and LPRP is obtained by dividing the LPR using its Hilbert envelope. The sine-weighted cepstrum estimators (SWCE) with six tapers are considered for multitaper MFCC feature extraction. The Gaussian mixture model–universal background

APA, Harvard, Vancouver, ISO, and other styles

44

Chandra, Wenripin, Ken Ken, Osfredo Quinn, and Irpan Adiputra Pardosi. "Human Age Estimation Through Audio Utilising MFCC and RNN." SinkrOn 8, no. 3 (2023): 1852–62. http://dx.doi.org/10.33395/sinkron.v8i3.12656.

Full text

Abstract:

Age is one of human main attributes. Age is important factor to improve communication experience. Age estimation has been used in several applications to improve user experience. Therefore, an approach is needed to estimate the user age, one of which is through audio. In this study, Mel Frequency Cepstrum Coefficients (MFCC) and Recurrent Neural Network (RNN) will be used to estimate age through audio. MFCC is used to get features from audio data, while RNN is used to estimate age. Dataset used here was taken from corpus of user speech data on the Common Voice website. This study shows that MF

APA, Harvard, Vancouver, ISO, and other styles

45

Wang, Xiao, and JingZhao Li. "Fault Diagnosis of Mine Hoist Based on MFCC-SVDD." Highlights in Science, Engineering and Technology 38 (March 16, 2023): 1116–22. http://dx.doi.org/10.54097/hset.v38i.6013.

Full text

Abstract:

In the field of coal mine production, mine hoist plays a very important role in the whole mine transportation engineering. Its safety and stability directly affect the production efficiency of coal mine and the life safety of staff. In view of this, a fault diagnosis method of mine hoist based on MFCC-SVDD is proposed. By collecting the audio signal of the elevator, MFCC algorithm was used to extract the sound signal of multiple channels and the MEL frequency cepstrum coefficient was used to extract the fault characteristic parameters. Based on the one-class classifier SVDD, the hypersphere of

APA, Harvard, Vancouver, ISO, and other styles

46

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

47

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

48

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

49

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

50

Sanjaya, WS Mada, and Dyah Anggraeni. "Sistem Kontrol Robot Arm 5 DOF Berbasis Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients (MFCC) dan Adaptive Neuro-Fuzzy Inference System (ANFIS)." Wahana Fisika 1, no. 2 (2016): 152. http://dx.doi.org/10.17509/wafi.v1i2.4277.

Full text

Abstract:

Telah dilakukan penelitian yang menggambarkan implementasi pengenalan pola suara untuk mengontrol gerak robot arm 5 DoF dalam mengambil dan menyimpan benda. Dalam penelitian ini metode yang digunakan adalah Mel-Frequency Cepstrum Coefficients (MFCC) dan Adaptive Neuro-Fuzzy Inferense System (ANFIS). Metode MFCC digunakan untuk ekstraksi ciri sinyal suara, sedangkan ANFIS digunakan sebagai metode pembelajaran untuk pengenalan pola suara. Pada proses pembelajaran ANFIS data latih yang digunakan sebanyak 6 ciri. Data suara terlatih dan data suara tak terlatih digunakan untuk pengujian sistem peng

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!