Artificial Intelligent Systems and Machine Learning

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Applicational Areas of MFCC


Affiliations
1 Department of Computer Science, Guru Gobind Singh Indraprastha University, New Delhi, India
     

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Mel Frequency Cepstral Coefficient is a very common and capable technique for signal processing. It is the basic method used for extracting the features of the voice signal .It is a powerful and popular acoustic vector that is used to represent and recognize the voice features and characteristics of the speaker. Mel-frequency cepstral coefficients are the coefficients that collectively represent the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. In this paper, we study about the various applications of MFCCs. The methods which were briefly studied include Vector Quantization (VQ), K Nearest Nieghbor (KNN), Dynamic Time Wrapping (DTW), Multi-Layered Perceptron (MLP), Gaussian Mixture Model (GMM), Support Vector Machine (SVM), Hidden Markov Model.


Keywords

Mel-Frequency Cepstral Coefficient, Speech Recognition Applications, Speaker Recognition Applications, Medical Applications, Clustering Classifiers.
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  • Applicational Areas of MFCC

Abstract Views: 317  |  PDF Views: 5

Authors

Preeti Kapoor
Department of Computer Science, Guru Gobind Singh Indraprastha University, New Delhi, India
Narina Thakur
Department of Computer Science, Guru Gobind Singh Indraprastha University, New Delhi, India

Abstract


Mel Frequency Cepstral Coefficient is a very common and capable technique for signal processing. It is the basic method used for extracting the features of the voice signal .It is a powerful and popular acoustic vector that is used to represent and recognize the voice features and characteristics of the speaker. Mel-frequency cepstral coefficients are the coefficients that collectively represent the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. In this paper, we study about the various applications of MFCCs. The methods which were briefly studied include Vector Quantization (VQ), K Nearest Nieghbor (KNN), Dynamic Time Wrapping (DTW), Multi-Layered Perceptron (MLP), Gaussian Mixture Model (GMM), Support Vector Machine (SVM), Hidden Markov Model.


Keywords


Mel-Frequency Cepstral Coefficient, Speech Recognition Applications, Speaker Recognition Applications, Medical Applications, Clustering Classifiers.

References