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.
Ayllón, David; Gil-Pita, Roberto; Rosa-Zurera, Manuel
Affiliation: University of Alcalá, Alcalá de Henares, Madrid, Spain
AES Convention: 136 (April 2014) Paper Number: 9035
Publication Date: April 25, 2014
Subject: Signal Processing
No AES members have commented on this paper yet.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.