A Model-Based Hearing Compensation Method Using a Self-Supervised Framework

Yadong Niu; Nan Li; Xihong Wu; Jing Chen

doi:10.1109/ICASSP49357.2023.10095767

A Model-Based Hearing Compensation Method Using a Self-Supervised Framework

Yadong Niu^*
, Nan Li
, Xihong Wu
, Jing Chen^*

^*Corresponding author for this work

Peking University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical hearing thresholds. This paper proposes a model-based hearing compensation method using a self-supervised framework with a given auditory model. The influence of outer/inner hair cells dysfunction was simulated in the auditory model. And then, a neural network was trained to compensate for the given hearing impairment. Both objective and subjective experiments were conducted to evaluate the present method, and the results showed that listeners are sensitive to the parameter controlling the contribution of outer hair cells dysfunction. Additionally, the result indicated that listeners significantly preferred the speech processed by the proposed method to the traditional perspective fitting.

Original language	English
Title of host publication	ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728163277
DOIs	https://doi.org/10.1109/ICASSP49357.2023.10095767
Publication status	Published - 2023
Externally published	Yes
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2023-June
ISSN (Print)	1520-6149

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	4/06/23 → 10/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Hearing compensation
self-supervised learning
speech quality

Access to Document

10.1109/ICASSP49357.2023.10095767

Cite this

Niu, Y., Li, N., Wu, X., & Chen, J. (2023). A Model-Based Hearing Compensation Method Using a Self-Supervised Framework. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP49357.2023.10095767

Niu, Yadong ; Li, Nan ; Wu, Xihong et al. / A Model-Based Hearing Compensation Method Using a Self-Supervised Framework. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{d390b9394f8b43b293c2119435139772,

title = "A Model-Based Hearing Compensation Method Using a Self-Supervised Framework",

abstract = "Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical hearing thresholds. This paper proposes a model-based hearing compensation method using a self-supervised framework with a given auditory model. The influence of outer/inner hair cells dysfunction was simulated in the auditory model. And then, a neural network was trained to compensate for the given hearing impairment. Both objective and subjective experiments were conducted to evaluate the present method, and the results showed that listeners are sensitive to the parameter controlling the contribution of outer hair cells dysfunction. Additionally, the result indicated that listeners significantly preferred the speech processed by the proposed method to the traditional perspective fitting.",

keywords = "Hearing compensation, self-supervised learning, speech quality",

author = "Yadong Niu and Nan Li and Xihong Wu and Jing Chen",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",

year = "2023",

doi = "10.1109/ICASSP49357.2023.10095767",

language = "English",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",

address = "United States",

}

Niu, Y, Li, N, Wu, X & Chen, J 2023, A Model-Based Hearing Compensation Method Using a Self-Supervised Framework. in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Rhodes Island, Greece, 4/06/23. https://doi.org/10.1109/ICASSP49357.2023.10095767

A Model-Based Hearing Compensation Method Using a Self-Supervised Framework. / Niu, Yadong; Li, Nan; Wu, Xihong et al.
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Model-Based Hearing Compensation Method Using a Self-Supervised Framework

AU - Niu, Yadong

AU - Li, Nan

AU - Wu, Xihong

AU - Chen, Jing

PY - 2023

Y1 - 2023

N2 - Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical hearing thresholds. This paper proposes a model-based hearing compensation method using a self-supervised framework with a given auditory model. The influence of outer/inner hair cells dysfunction was simulated in the auditory model. And then, a neural network was trained to compensate for the given hearing impairment. Both objective and subjective experiments were conducted to evaluate the present method, and the results showed that listeners are sensitive to the parameter controlling the contribution of outer hair cells dysfunction. Additionally, the result indicated that listeners significantly preferred the speech processed by the proposed method to the traditional perspective fitting.

AB - Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical hearing thresholds. This paper proposes a model-based hearing compensation method using a self-supervised framework with a given auditory model. The influence of outer/inner hair cells dysfunction was simulated in the auditory model. And then, a neural network was trained to compensate for the given hearing impairment. Both objective and subjective experiments were conducted to evaluate the present method, and the results showed that listeners are sensitive to the parameter controlling the contribution of outer hair cells dysfunction. Additionally, the result indicated that listeners significantly preferred the speech processed by the proposed method to the traditional perspective fitting.

KW - Hearing compensation

KW - self-supervised learning

KW - speech quality

UR - https://www.scopus.com/pages/publications/85177607364

U2 - 10.1109/ICASSP49357.2023.10095767

DO - 10.1109/ICASSP49357.2023.10095767

M3 - Conference contribution

AN - SCOPUS:85177607364

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

Y2 - 4 June 2023 through 10 June 2023

ER -

Niu Y, Li N, Wu X, Chen J. A Model-Based Hearing Compensation Method Using a Self-Supervised Framework. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP49357.2023.10095767

A Model-Based Hearing Compensation Method Using a Self-Supervised Framework

Abstract

Publication series

Conference

UN SDGs

Keywords

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