A Parametric Approach for Classification of Distortions in Pathological Voices

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Resumé

In biomedical acoustics, distortion in voice signals, commonly pre- sent during acquisition and transmission, adversely affects acoustic features extracted from pathological voice. Information on the type of distortion can help in compensating for its effects. This paper proposes a new approach to detecting four major types of commonly encountered distortion in remote analysis of pathological voice, na- mely background noise, reverberation, clipping and coding. In this approach, by applying factor analysis to Gaussian mixture model mean supervectors, distortions in variable-duration recordings are modeled by fixed-length, low-dimensional channel vectors. Then, linear discriminant analysis (LDA) is used to remove the remaining nuisance effects in the channel vectors. Finally, two different clas- sifiers, namely support vector machines and probabilistic LDA clas- sify the different types of distortion. Experimental results obtained using Parkinson’s voices, as an example of pathological voice, show 11.4% relative improvement in performance over systems which di- rectly use acoustic features for distortion classification.
OriginalsprogEngelsk
Titel2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Antal sider5
ForlagIEEE
Publikationsdato10 sep. 2018
Sider286-290
Artikelnummer8461316
ISBN (Trykt)978-1-5386-4659-5
ISBN (Elektronisk)978-1-5386-4658-8
DOI
StatusUdgivet - 10 sep. 2018
Begivenhed2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada
Varighed: 15 apr. 201820 apr. 2018
https://2018.ieeeicassp.org/

Konference

Konference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
LandCanada
ByCalgary
Periode15/04/201820/04/2018
Internetadresse
NavnI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Fingerprint

Discriminant analysis
Acoustic distortion
Acoustics
Reverberation
Factor analysis
Acoustic noise
Support vector machines
Classifiers

Citer dette

Poorjam, A. H., Little, M. A., Jensen, J. R., & Christensen, M. G. (2018). A Parametric Approach for Classification of Distortions in Pathological Voices. I 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (s. 286-290). [8461316] IEEE. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings https://doi.org/10.1109/ICASSP.2018.8461316
Poorjam, Amir Hossein ; Little, Max A ; Jensen, Jesper Rindom ; Christensen, Mads Græsbøll. / A Parametric Approach for Classification of Distortions in Pathological Voices. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. s. 286-290 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).
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title = "A Parametric Approach for Classification of Distortions in Pathological Voices",
abstract = "In biomedical acoustics, distortion in voice signals, commonly pre- sent during acquisition and transmission, adversely affects acoustic features extracted from pathological voice. Information on the type of distortion can help in compensating for its effects. This paper proposes a new approach to detecting four major types of commonly encountered distortion in remote analysis of pathological voice, na- mely background noise, reverberation, clipping and coding. In this approach, by applying factor analysis to Gaussian mixture model mean supervectors, distortions in variable-duration recordings are modeled by fixed-length, low-dimensional channel vectors. Then, linear discriminant analysis (LDA) is used to remove the remaining nuisance effects in the channel vectors. Finally, two different clas- sifiers, namely support vector machines and probabilistic LDA clas- sify the different types of distortion. Experimental results obtained using Parkinson’s voices, as an example of pathological voice, show 11.4{\%} relative improvement in performance over systems which di- rectly use acoustic features for distortion classification.",
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Poorjam, AH, Little, MA, Jensen, JR & Christensen, MG 2018, A Parametric Approach for Classification of Distortions in Pathological Voices. i 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)., 8461316, IEEE, I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, s. 286-290, Calgary, Canada, 15/04/2018. https://doi.org/10.1109/ICASSP.2018.8461316

A Parametric Approach for Classification of Distortions in Pathological Voices. / Poorjam, Amir Hossein; Little, Max A; Jensen, Jesper Rindom; Christensen, Mads Græsbøll.

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. s. 286-290 8461316 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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Poorjam AH, Little MA, Jensen JR, Christensen MG. A Parametric Approach for Classification of Distortions in Pathological Voices. I 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2018. s. 286-290. 8461316. (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings). https://doi.org/10.1109/ICASSP.2018.8461316