Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)

Abstract: 

State-of-the-art hearing aids (HA) are limited in recognizing acoustic environments. Much effort is spent on research to improve listening experience for HA users in every acoustic situation. There is, however, no dedicated public database to train acoustic environment recognition algorithms with a specific focus on HA applications accounting for their requirements. Existing acoustic scene classification databases are inappropriate for HA signal processing. In this work we propose a novel, binaural HA acoustic environment recognition data set (HEAR-DS) suitable for the environment recognition needs of HAs. We present the
details about each individual environment provided within the data set. To show separability of these acoustic environments we trained a group of deep neural network-based classifiers which vary in complexity. The obtained classification accuracies provide a reliable indicator about the validity and separability of the provided data set. Finally, as we do not aim at providing the best possible neural network architecture to perform such a classification, but propose solely a novel data set, further research
is needed to streamline such networks and optimize them for robustness, real-time and limited computational capability to fit into modern HAs.

up
0 users have voted:

Paper Details

Authors:
Kamil Adiloğlu, Jörg-Hendrik Bach
Submitted On:
18 May 2020 - 7:01am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Andreas Hüwel
Paper Code:
AUD-P9.2
Document Year:
2020
Cite

Document Files

https://download.hoertech.de/hear-ds-data/HEAR-DS/RawAudioCuts/doc/icassp2020-hear-ds-presentation-huewel.mp4

(24)

Subscribe

[1] Kamil Adiloğlu, Jörg-Hendrik Bach, "Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5392. Accessed: Jul. 08, 2020.
@article{5392-20,
url = {http://sigport.org/5392},
author = {Kamil Adiloğlu; Jörg-Hendrik Bach },
publisher = {IEEE SigPort},
title = {Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)},
year = {2020} }
TY - EJOUR
T1 - Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)
AU - Kamil Adiloğlu; Jörg-Hendrik Bach
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5392
ER -
Kamil Adiloğlu, Jörg-Hendrik Bach. (2020). Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS). IEEE SigPort. http://sigport.org/5392
Kamil Adiloğlu, Jörg-Hendrik Bach, 2020. Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS). Available at: http://sigport.org/5392.
Kamil Adiloğlu, Jörg-Hendrik Bach. (2020). "Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)." Web.
1. Kamil Adiloğlu, Jörg-Hendrik Bach. Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS) [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5392