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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

7 Citations (Scopus)
83 Downloads (Pure)

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 publicationProc. INTERSPEECH 2017
Number of pages5
Publication dateAug 2017
Pages289-293
DOIs
Publication statusPublished - Aug 2017
EventInterspeech 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017
http://www.interspeech2017.org/

Conference

ConferenceInterspeech 2017
CountrySweden
CityStockholm
Period20/08/201724/08/2017
Internet address
SeriesProceedings of the International Conference on Spoken Language Processing
ISSN1990-9772

Fingerprint

Processing
Reverberation
Signal analysis
Acoustic noise
Support vector machines
Classifiers

Keywords

  • distortion analysis
  • MFCC
  • remote biomedical voice assessment
  • Support vector machine (SVM)

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 Proc. INTERSPEECH 2017 (pp. 289-293). Proceedings of the International Conference on Spoken Language Processing 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. Proc. INTERSPEECH 2017. 2017. pp. 289-293 (Proceedings of the International Conference on Spoken Language Processing).
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Poorjam, AH, Jensen, JR, Little, MA & Christensen, MG 2017, Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis. in Proc. INTERSPEECH 2017. Proceedings of the International Conference on Spoken Language Processing, pp. 289-293, Interspeech 2017, Stockholm, Sweden, 20/08/2017. 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.

Proc. INTERSPEECH 2017. 2017. p. 289-293 (Proceedings of the International Conference on Spoken Language Processing).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Poorjam AH, Jensen JR, Little MA, Christensen MG. Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis. In Proc. INTERSPEECH 2017. 2017. p. 289-293. (Proceedings of the International Conference on Spoken Language Processing). https://doi.org/10.21437/Interspeech.2017-378