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MFCC in audio signal processing for voice disorder: a review

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Abstract

Voice Disorder or Dysphonia has caught the attention of audio signal process engineers and researchers. The efficiency of several feature extraction and classifier implementation techniques in identifying voice abnormalities has been investigated. Mel-Frequency Cepstral Coefficient (MFCC) has been extensively used as a feature extractor. This paper adopts a Comparative Review Method to assess the effectiveness of feature extraction and classifier methods in detecting voice disorders. By examining the pairing of the Mel-Frequency Cepstral Coefficient (MFCC) with various classifiers, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and other online or commercial classifiers, the study aims to review the robustness of MFCC in this context. The study also recognizes the significance of choosing the right database in light of the various aetiologies of pathological illnesses and its possible influence on the efficacy of voice disorder detection.

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Abbreviations

MFCC:

Mel-Frequency Cepstral Coefficient

SVM:

Support Vector Machine

ANN:

Artificial Neural Network

DT:

Decision Tree

FT:

Fourier Transform

DWT:

Discrete Wavelet Transform

DFT:

Discrete Fourier Transform

OSLEM:

Online Sequential Extreme Learning Machine

MDVP:

Multi-Dimensional Voice Parameter

HNR:

Harmonic-to-Noise Ratio

LDA:

Linear Discriminant Analysis

KNN:

K-Nearest Neighbour

DNN:

Deep Neural Network

DCT:

Discrete Cosine Transform

HTK:

Hidden Markov Model Toolkit

ZCR:

Zero Crossing Rate

SVD:

Saarbruecken Voice Database

AVPD:

Arabic Voice Pathology Database

MEEI:

Massachusetts Eye and Ear Infirmary Voice Disorders Database

M.S.S.:

Manjit Singh Sidhu

N.A.A.L.:

Nur Atiqah Abdul Latib

K.K.S.:

Kirandeep Kaur Sidhu

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Acknowledgements

This work is supported by Yayasan Canselor Uniten (YCU) Seeding Fund No. 202210016YCU and Innovation & Research Management Centre (iRMC).

Funding

This work received funding from Yayasan Canselor Uniten (YCU) Seeding Fund No. 202210016YCU and Innovation & Research Management Centre (iRMC).

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Authors and Affiliations

  1. Department of Graphics and Multimedia, College of Computing and Informatics, University Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan Kajang - Puchong, Kajang, Selangor, 43000, Malaysia

    Manjit Singh Sidhu & Nur Atiqah Abdul Latib

  2. MK-Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman (UTAR), Kajang, Selangor, Malaysia

    Kirandeep Kaur Sidhu

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  1. Manjit Singh Sidhu
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Contributions

Conceptualization: N.A.A.L. and M.S.S.; Literature search: N.A.A.L.; Data analysis: N.A.A.L. and K.K.S.; Writing -original draft preparation: N.A.A.L.; Writing -review and editing: M.S.S. and K.K.S; Writing- final draft: N.A.A.L. and K.K.S; Supervision: M.S.S. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Manjit Singh Sidhu.

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Sidhu, M.S., Latib, N.A.A. & Sidhu, K.K. MFCC in audio signal processing for voice disorder: a review. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19253-1

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