A machine learning approach for computationally and energy efficient speech enhancement in binaural hearing aids

David Ayllón; Roberto Gil-Pita; Manuel Rosa-Zurera

ICASSP 2016accepted0 citations

A machine learning approach for computationally and energy efficient speech enhancement in binaural hearing aids

David Ayllón, Roberto Gil-Pita, Manuel Rosa-Zurera

Abstract

A binaural speech enhancement algorithm that combines superdirective beamforming with time-frequency (TF) masking is proposed. Supervised machine learning is used to design a speech/noise classifier that estimates the ideal binary mask (IBM), which is further softened to reduce musical noise. The method is energy-efficient in two ways: the computational complexity is limited and the wireless data transmission optimized. The experimental work demonstrates the ability of the method to increase the intelligibility of speech corrupted by different types of noise in low SNR scenarios.

BibTeX

@inproceedings{icassp2016_amachinelearning,
  title = {A machine learning approach for computationally and energy efficient speech enhancement in binaural hearing aids},
  author = {David Ayllón and Roberto Gil-Pita and Manuel Rosa-Zurera},
  booktitle = {ICASSP 2016},
  year = {2016}
}