Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis

Amir Hossein Poorjam, Jesper Rindom Jensen, Max A. Little, Mads Græsbøll Christensen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Advances in speech signal analysis facilitate the development of techniques for remote biomedical voice assessment. However, the performance of these techniques is affected by noise and distortion in signals. In this paper, we focus on the vowel /a/ as the most widely-used voice signal for pathological voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such voice signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given voice signal. Experimental results obtained from the healthy and Parkinson's voices show the effectiveness of the proposed approach in distortion detection and classification.

Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017
Pages289-293
Number of pages5
Volume2017-August
DOIs
Publication statusPublished - 24 Aug 2017
Event18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017

Conference

Conference18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017
CountrySweden
CityStockholm
Period20/08/1724/08/17

Fingerprint

Preprocessing
Processing
Speech Analysis
Clipping
Signal Analysis
Voice
Speech Signal
Coefficient
Reverberation
Signal analysis
Acoustic noise
Support Vector Machine
Support vector machines
Compression
Classifier
Classifiers
Experimental Results

Bibliographical note

Copyright © 2017 ISCA

Keywords

  • Distortion analysis
  • MFCC
  • Remote biomedical voice assessment
  • Support vector machine

Cite this

Poorjam, A. H., Jensen, J. R., Little, M. A., & Christensen, M. G. (2017). Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 (Vol. 2017-August, pp. 289-293) https://doi.org/10.21437/Interspeech.2017-378
Poorjam, Amir Hossein ; Jensen, Jesper Rindom ; Little, Max A. ; Christensen, Mads Græsbøll. / Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017. Vol. 2017-August 2017. pp. 289-293
@inproceedings{7e559e72d48f43c8aa052383be5346a9,
title = "Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis",
abstract = "Advances in speech signal analysis facilitate the development of techniques for remote biomedical voice assessment. However, the performance of these techniques is affected by noise and distortion in signals. In this paper, we focus on the vowel /a/ as the most widely-used voice signal for pathological voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such voice signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given voice signal. Experimental results obtained from the healthy and Parkinson's voices show the effectiveness of the proposed approach in distortion detection and classification.",
keywords = "Distortion analysis, MFCC, Remote biomedical voice assessment, Support vector machine",
author = "Poorjam, {Amir Hossein} and Jensen, {Jesper Rindom} and Little, {Max A.} and Christensen, {Mads Gr{\ae}sb{\o}ll}",
note = "Copyright {\circledC} 2017 ISCA",
year = "2017",
month = "8",
day = "24",
doi = "10.21437/Interspeech.2017-378",
language = "English",
volume = "2017-August",
pages = "289--293",
booktitle = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017",

}

Poorjam, AH, Jensen, JR, Little, MA & Christensen, MG 2017, Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis. in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017. vol. 2017-August, pp. 289-293, 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017, Stockholm, Sweden, 20/08/17. https://doi.org/10.21437/Interspeech.2017-378

Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis. / Poorjam, Amir Hossein; Jensen, Jesper Rindom; Little, Max A.; Christensen, Mads Græsbøll.

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017. Vol. 2017-August 2017. p. 289-293.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis

AU - Poorjam, Amir Hossein

AU - Jensen, Jesper Rindom

AU - Little, Max A.

AU - Christensen, Mads Græsbøll

N1 - Copyright © 2017 ISCA

PY - 2017/8/24

Y1 - 2017/8/24

N2 - Advances in speech signal analysis facilitate the development of techniques for remote biomedical voice assessment. However, the performance of these techniques is affected by noise and distortion in signals. In this paper, we focus on the vowel /a/ as the most widely-used voice signal for pathological voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such voice signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given voice signal. Experimental results obtained from the healthy and Parkinson's voices show the effectiveness of the proposed approach in distortion detection and classification.

AB - Advances in speech signal analysis facilitate the development of techniques for remote biomedical voice assessment. However, the performance of these techniques is affected by noise and distortion in signals. In this paper, we focus on the vowel /a/ as the most widely-used voice signal for pathological voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such voice signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given voice signal. Experimental results obtained from the healthy and Parkinson's voices show the effectiveness of the proposed approach in distortion detection and classification.

KW - Distortion analysis

KW - MFCC

KW - Remote biomedical voice assessment

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=85039165515&partnerID=8YFLogxK

U2 - 10.21437/Interspeech.2017-378

DO - 10.21437/Interspeech.2017-378

M3 - Conference contribution

AN - SCOPUS:85039165515

VL - 2017-August

SP - 289

EP - 293

BT - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017

ER -

Poorjam AH, Jensen JR, Little MA, Christensen MG. Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017. Vol. 2017-August. 2017. p. 289-293 https://doi.org/10.21437/Interspeech.2017-378