Technical Program

Paper Detail

Paper:	MLSP-P6.4
Session:	Biomedical and Other Applications
Time:	Friday, May 19, 16:30 - 18:30
Presentation:	Poster
Topic:	Machine Learning for Signal Processing: Biomedical Applications and Neural Engineering
Title:	AUGMENTING INFORMATION CHANNELS IN HEARING AIDS AND COCHLEAR IMPLANTS UNDER ADVERSE CONDITIONS
Authors:	Yasir Suhail, Johns Hopkins University, United States; Karim Oweiss, Michigan State University, United States
Abstract:	We conceptualize a new signal processing strategy to better represent the temporal and spectral cues in speech signals for Hearing Aid (HA) and Cochlear Implant (CI) applications under severe adverse conditions. The proposed approach rests on two well studied methods for signal separation and noise suppression, namely, the denoising and function approximation capabilities of the wavelet transform, blended with signal subspace decomposition through low rank approximation. The technique targets suppression of “competing voice” type noises. A cost function is defined to obtain a “best basis” representation of the desired speech signal for which an inherent invariance property of the signal subspace is observed. This allows better separation of the speech-like noise in contrast to classical bandpass filtering currently employed in CI and HA devices. We demonstrate the efficiency of the proposed method in capturing the rapid dynamics of speech signals, while minimizing the masking effects of noise, in addition to improved recognition rates in normal hearing listeners. The technique remains to be tested on actual patients.