Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson’s Disease Detection

Amir Hossein Poorjam, Mathew S. Kavalekalam, Liming Shi, Raykov Yordan, Jesper B. Jensen, Max A Little, Mads Græsbøll Christensen

Research output: Working paper

Abstract

The performance of voice-based Parkinson’s disease
(PD) detection systems degrades when there is an acoustic mismatch
between training and operating conditions caused mainly
by degradation in test signals. In this paper, we address this
mismatch by considering three types of degradation commonly
encountered in remote voice analysis, namely background noise,
reverberation and nonlinear distortion, and investigate how
these degradations influence the performance of a PD detection
system. Given that the specific degradation is known, we explore
the effectiveness of a variety of enhancement algorithms in
compensating this mismatch and improving the PD detection
accuracy. Then, we propose two approaches to automatically
control the quality of recordings by identifying the presence
and type of short-term and long-term degradations and protocol
violations in voice signals. Finally, we experiment with using
the proposed quality control methods to inform the choice of
enhancement algorithm. Experimental results using the voice
recordings of the mPower mobile PD data set under different
degradation conditions show the effectiveness of the quality control
approaches in selecting an appropriate enhancement method
and, consequently, in improving the PD detection accuracy. This
study is a step towards the development of a remote PD detection
system capable of operating in unseen acoustic environments.
Original languageEnglish
Publication statusPublished - 28 May 2019

Fingerprint

Quality control
Degradation
Acoustics
Nonlinear distortion
Reverberation
Acoustic noise
Experiments

Bibliographical note

© 2019 The Authors

Cite this

Poorjam, A. H., Kavalekalam, M. S., Shi, L., Yordan, R., Jensen, J. B., Little, M. A., & Christensen, M. G. (2019). Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson’s Disease Detection.
Poorjam, Amir Hossein ; Kavalekalam, Mathew S. ; Shi, Liming ; Yordan, Raykov ; Jensen, Jesper B. ; Little, Max A ; Christensen, Mads Græsbøll. / Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson’s Disease Detection. 2019.
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abstract = "The performance of voice-based Parkinson’s disease(PD) detection systems degrades when there is an acoustic mismatchbetween training and operating conditions caused mainlyby degradation in test signals. In this paper, we address thismismatch by considering three types of degradation commonlyencountered in remote voice analysis, namely background noise,reverberation and nonlinear distortion, and investigate howthese degradations influence the performance of a PD detectionsystem. Given that the specific degradation is known, we explorethe effectiveness of a variety of enhancement algorithms incompensating this mismatch and improving the PD detectionaccuracy. Then, we propose two approaches to automaticallycontrol the quality of recordings by identifying the presenceand type of short-term and long-term degradations and protocolviolations in voice signals. Finally, we experiment with usingthe proposed quality control methods to inform the choice ofenhancement algorithm. Experimental results using the voicerecordings of the mPower mobile PD data set under differentdegradation conditions show the effectiveness of the quality controlapproaches in selecting an appropriate enhancement methodand, consequently, in improving the PD detection accuracy. Thisstudy is a step towards the development of a remote PD detectionsystem capable of operating in unseen acoustic environments.",
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Poorjam, AH, Kavalekalam, MS, Shi, L, Yordan, R, Jensen, JB, Little, MA & Christensen, MG 2019 'Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson’s Disease Detection'.

Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson’s Disease Detection. / Poorjam, Amir Hossein; Kavalekalam, Mathew S.; Shi, Liming; Yordan, Raykov; Jensen, Jesper B.; Little, Max A; Christensen, Mads Græsbøll.

2019.

Research output: Working paper

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T1 - Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson’s Disease Detection

AU - Poorjam, Amir Hossein

AU - Kavalekalam, Mathew S.

AU - Shi, Liming

AU - Yordan, Raykov

AU - Jensen, Jesper B.

AU - Little, Max A

AU - Christensen, Mads Græsbøll

N1 - © 2019 The Authors

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N2 - The performance of voice-based Parkinson’s disease(PD) detection systems degrades when there is an acoustic mismatchbetween training and operating conditions caused mainlyby degradation in test signals. In this paper, we address thismismatch by considering three types of degradation commonlyencountered in remote voice analysis, namely background noise,reverberation and nonlinear distortion, and investigate howthese degradations influence the performance of a PD detectionsystem. Given that the specific degradation is known, we explorethe effectiveness of a variety of enhancement algorithms incompensating this mismatch and improving the PD detectionaccuracy. Then, we propose two approaches to automaticallycontrol the quality of recordings by identifying the presenceand type of short-term and long-term degradations and protocolviolations in voice signals. Finally, we experiment with usingthe proposed quality control methods to inform the choice ofenhancement algorithm. Experimental results using the voicerecordings of the mPower mobile PD data set under differentdegradation conditions show the effectiveness of the quality controlapproaches in selecting an appropriate enhancement methodand, consequently, in improving the PD detection accuracy. Thisstudy is a step towards the development of a remote PD detectionsystem capable of operating in unseen acoustic environments.

AB - The performance of voice-based Parkinson’s disease(PD) detection systems degrades when there is an acoustic mismatchbetween training and operating conditions caused mainlyby degradation in test signals. In this paper, we address thismismatch by considering three types of degradation commonlyencountered in remote voice analysis, namely background noise,reverberation and nonlinear distortion, and investigate howthese degradations influence the performance of a PD detectionsystem. Given that the specific degradation is known, we explorethe effectiveness of a variety of enhancement algorithms incompensating this mismatch and improving the PD detectionaccuracy. Then, we propose two approaches to automaticallycontrol the quality of recordings by identifying the presenceand type of short-term and long-term degradations and protocolviolations in voice signals. Finally, we experiment with usingthe proposed quality control methods to inform the choice ofenhancement algorithm. Experimental results using the voicerecordings of the mPower mobile PD data set under differentdegradation conditions show the effectiveness of the quality controlapproaches in selecting an appropriate enhancement methodand, consequently, in improving the PD detection accuracy. Thisstudy is a step towards the development of a remote PD detectionsystem capable of operating in unseen acoustic environments.

M3 - Working paper

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Poorjam AH, Kavalekalam MS, Shi L, Yordan R, Jensen JB, Little MA et al. Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson’s Disease Detection. 2019 May 28.