Generalization of supervised learning for binary mask estimation

Abstract

This paper addresses the problem of speech segregation by estimating the ideal binary mask (IBM) from noisy speech. Two methods will be compared, one supervised learning approach that incorporates a priori knowledge about the feature distribution observed during training. The second method solely relies on a frame-based speech presence probability (SPP) es… (More)
DOI: 10.1109/IWAENC.2014.6953357

Topics

2 Figures and Tables

Slides referencing similar topics