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The improvement of speech intelligibility in hearing aids is a complex and unsolved problem. The recent development of binaural hearing aids allows the design of speech enhancement algorithms to take advantages of the benefits of binaural hearing. In this paper a novel source separation algorithm for binaural speech enhancement based on supervised machine learning and time-frequency masking is presented. The proposed algorithm requires less than 10% of the available instructions for signal processing in a state-of-the-art hearing aid and obtains good separation performance in terms of WDO for low SNR.
Author (s): Ayllón, David; Gil-Pita, Roberto; Rosa-Zurera, Manuel
Affiliation:
University of Alcalá, Alcalá de Henares, Madrid, Spain
(See document for exact affiliation information.)
AES Convention: 136
Paper Number:9035
Publication Date:
2014-04-06
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Session subject:
Signal Processing
Permalink: https://aes2.org/publications/elibrary-page/?id=17182
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Ayllón, David; Gil-Pita, Roberto; Rosa-Zurera, Manuel; 2014; Computationally-Efficient Speech Enhancement Algorithm for Binaural Hearing Aids [PDF]; University of Alcalá, Alcalá de Henares, Madrid, Spain; Paper 9035; Available from: https://aes2.org/publications/elibrary-page/?id=17182
Ayllón, David; Gil-Pita, Roberto; Rosa-Zurera, Manuel; Computationally-Efficient Speech Enhancement Algorithm for Binaural Hearing Aids [PDF]; University of Alcalá, Alcalá de Henares, Madrid, Spain; Paper 9035; 2014 Available: https://aes2.org/publications/elibrary-page/?id=17182
@article{ayllón2014computationally-efficient,
author={ayllón david and gil-pita roberto and rosa-zurera manuel},
journal={journal of the audio engineering society},
title={computationally-efficient speech enhancement algorithm for binaural hearing aids},
year={2014},
number={9035},
month={april},}