Modeling Binaural Unmasking of Speech Using a Blind Binaural Processing Stage.

ABSTRACT: The equalization cancellation model is often used to predict the binaural masking level difference. Previously its application to speech in noise has required separate knowledge about the speech and noise signals to maximize the signal-to-noise ratio (SNR). Here, a novel, blind equalization cancellation model is introduced that can use the mixed signals. This approach does not require any assumptions about particular sound source directions. It uses different strategies for positive and negative SNRs, with the switching between the two steered by a blind decision stage utilizing modulation cues. The output of the model is a single-channel signal with enhanced SNR, which we analyzed using the speech intelligibility index to compare speech intelligibility predictions. In a first exper

SUBMITTER: Hauth CF 

PROVIDER: S-EPMC7734536 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature