Choosing an accurate number of mel frequency cepstral coefficients for audio classification purpose

2017 ◽  
Author(s):  
Lacrimioara Grama ◽  
Corneliu Rusu
Keyword(s):  
Audio Classification ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients

scholarly journals Covid Classification Using Audio Data

2021 ◽  
Vol 9 (10) ◽  
pp. 1633-1637
Author(s):  
Adwait Patil
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Image Data ◽  
Audio Classification ◽  
Mel Frequency Cepstral Coefficients ◽  
Audio Data ◽  
Cepstral Coefficients ◽  
Audio Files

Abstract: Coronavirus outbreak has affected the entire world adversely this project has been developed in order to help common masses diagnose their chances of been covid positive just by using coughing sound and basic patient data. Audio classification is one of the most interesting applications of deep learning. Similar to image data audio data is also stored in form of bits and to understand and analyze this audio data we have used Mel frequency cepstral coefficients (MFCCs) which makes it possible to feed the audio to our neural network. In this project we have used Coughvid a crowdsource dataset consisting of 27000 audio files and metadata of same amount of patients. In this project we have used a 1D Convolutional Neural Network (CNN) to process the audio and metadata. Future scope for this project will be a model that rates how likely it is that a person is infected instead of binary classification. Keywords: Audio classification, Mel frequency cepstral coefficients, Convolutional neural network, deep learning, Coughvid

scholarly journals Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

2020 ◽  
Vol 18 (2) ◽  
pp. 782
Author(s):  
Musab T. S. Al-Kaltakchi ◽  
Haithem Abd Al-Raheem Taha ◽  
Mohanad Abd Shehab ◽  
Mohamed A.M. Abdullah
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Gaussian Mixture ◽  
Identification Accuracy ◽  
Identification System ◽  
Good Representation ◽  
Mel Frequency Cepstral Coefficients ◽  
Normalization Methods ◽  
Cepstral Coefficients

<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

scholarly journals Reconocimiento de emociones en el habla

TecnoLógicas ◽  
2008 ◽  
pp. 113
Author(s):  
Julián D. Echeverry-Correa ◽  
Mauricio Morales-Pérez
Keyword(s):  
Emotional Speech ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients

Se presenta en este trabajo una metodología para la caracterización de la señal de voz aplicada al reconocimiento de estados emocionales. Son estudiadas cuatro emociones primarias (alegría, enojo, sorpresa y tristeza) y un estado neutral. Se realizó un análisis en el dominio temporal y un análisis acústico empleando los MFCC (Mel Frequency Cepstral Coefficients). Las pruebas comprueban la efectividad de la metodología en el reconocimiento de las emociones superando el reconocimiento realizado por un grupo de personas. Se obtiene un porcentaje de 94.00% de acierto en el reconocimiento de emociones trabajando sobre la base de SES (Spanish emotional speech).

scholarly journals The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool

2019 ◽  
Vol 9 (23) ◽  
pp. 5064 ◽  
Author(s):  
Marco Civera ◽  
Matteo Ferraris ◽  
Rosario Ceravolo ◽  
Cecilia Surace ◽  
Raimondo Betti
Keyword(s):  
Experimental Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Speech Processing ◽  
Speaker Recognition ◽  
Vibration Analysis ◽  
Monitoring Tool ◽  
Mel Frequency Cepstral Coefficients ◽  
Structural Health ◽  
Cepstral Coefficients

Recently, features and techniques from speech processing have started to gain increasing attention in the Structural Health Monitoring (SHM) community, in the context of vibration analysis. In particular, the Cepstral Coefficients (CCs) proved to be apt in discerning the response of a damaged structure with respect to a given undamaged baseline. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs). This approach, while efficient and still very common in applications, such as speech and speaker recognition, has been followed by other more advanced and competitive techniques for the same aims. The Teager-Kaiser Energy Cepstral Coefficients (TECCs) is one of these alternatives. These features are very closely related to MFCCs, but provide interesting and useful additional values, such as e.g., improved robustness with respect to noise. The goal of this paper is to introduce the use of TECCs for damage detection purposes, by highlighting their competitiveness with closely related features. Promising results from both numerical and experimental data were obtained.

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