EE Seminar: The Advantage of Beamformer Cochlear Noise Reduction Algorithm to the Hearing Impaired

Speaker: Carmi Shimon

M.Sc. student under the supervision of Prof. Miriam Furst-Yust

Wednesday, October 30, 2019 at 15:00

Room 011, Kitot Bldg., Faculty of Engineering

The Advantage of Beamformer Cochlear Noise Reduction Algorithm to the Hearing Impaired

Abstract

Hearing aid (HA) research still have the challenge of improving the ability of the   hearing impaired (HI) to understand speech in a noisy environment. Modern HAs use multiple-channel noise reduction algorithms to reduce the background noise. A decade ago we have developed a Cochlear Noise Reduction Algorithm (CNRA) that mimics the way the cochlea process acoustic signals. We have shown experimentally that the algorithm significantly helps HI people, but not normal hearing (NH) people. In the current study we introduce a new algorithm:  Beamformer Cochlear Noise Reduction Algorithm  (BCNRA) which includes a beamformer (the Frost algorithm) followed by CNRA.  BCNRA was evaluated theoretically and experimentally by using a database of 150 Hebrew sentences embedded in different noise types and SNRs. The theoretical evaluation included derivation of Segmental SNR (sSNR) of the input signals, followed by Frost and followed by BCNRA. The analysis yielded a significant improvement in  sSNR of BCNRA relative to Frost especially in those parts of the sentences that did not include speech. In the experimental evaluation subjects were asked to indicate the words they heard while listening to  noisy sentences. The subjects were a group of 10 young normal hearing (NH) people and 10 old HI people who regularly use HAs. In the average the NH yielded an improvement of about 30% in words identification following both Frost BCNRA. On the other hand, the HI yielded an improvement of 30% following Frost and 50% following BCNRA. The benefit of BCNRA to the HI is probably due to its ability to separate words in the noisy sentences.