Adaptive Cross-Channel Interference Cancellation on Blind Source Separation Outputs

Abstract

Despite an abundance of research outcomes of blind source separation (BSS) in many types of simulated environments, their performances are still not satisfiable to apply to the real environments. The major obstacle may seem the finite filter length of the assumed mixing model and the nonlinear sensor noises. This paper presents a two-step speech enhancement method with stereo microphone inputs. The first step performs a frequency-domain BSS algorithm with no prior knowledge of the mixed source signals and generates stereo outputs. The second step further removes the remaining cross-channel interference by a spectral cancellation approach using a probabilistic source absence/presence detection technique. The desired primary source is detected every frame of the signal, and the secondary source is estimated in the powerspectral domain using the other BSS output as a reference interference source. Then the secondary source is subtracted to remove the cross-channel interference. Our experimental results show good separation enhancement performances on the real recordings of speech and music signals compared to the conventional BSS methods.