A computationally-efficient single-channel speech enhancement algorithm for monaural hearing aids

Abstract

A computationally-efficient single-channel speech enhancement algorithm to improve intelligibility in monaural hearing aids is presented in this paper. The algorithm combines a novel set of features with a simple supervised machine learning technique to estimate the frequency-domain Wiener filter for noise reduction, using extremely low computational resources. Results show a noticeable intelligibility improvement in terms of PESQ score and SNR<sub>ESI</sub>, even for low input SNR, using only a 7% of the computational resources available in a state-of-the-art commercial hearing aid. The performance of the algorithm is comparable to the performance of current algorithms that use more computationally complex features and learning schemas.

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@article{Aylln2014ACS, title={A computationally-efficient single-channel speech enhancement algorithm for monaural hearing aids}, author={David Ayll{\'o}n and Roberto Gil-Pita and Manuel Utrilla-Manso and Manuel Rosa-Zurera}, journal={2014 22nd European Signal Processing Conference (EUSIPCO)}, year={2014}, pages={2050-2054} }