Speech Recovery Using Adaptive Noise Canceller with Multiple Optimized Primary Channels

Abstract

In recent years, speech enhancement in the presence of very harsh noise has received a lot of attention. In this paper, an adaptive noise canceller (ANC) having a single-reference and multi-primary channels (SRMP) is introduced to recover the speech signal from its contaminated measurements. This SRMPANC takes a bone-conducted (BC) signal as its reference and adopts multiple noisy air-conducted (AC) speech measurements as its primary signals. The multiple primary channels are connected by a linear combiner (LC) which is optimized and updated by a recursive least square (RLS) algorithm. A linear or a nonlinear filter (e.g. FIR, Volterra, etc.) is equipped in the SRMP-ANC, that is updated by an LMS or LMS-like algorithm. Extensive simulations with real bone- and air-conducted speech measurements are performed to demonstrate the effectiveness of the proposed SRMP-ANC. It is revealed that the speech recovery quality is significantly improved as the number of primary channels is increased in terms of both mutual correlation and mean square error (MSE) between the AC speech signal and its recovered version.