Joint Multi-Microphone Speech Dereverberation and Noise Reduction Using Integrated Sidelobe Cancellation and Linear Prediction
Thomas Dietzen, Simon Doclo, Marc Moonen, Toon van Waterschoot · 2018
In multi-microphone speech enhancement, reverberation and noise are commonly suppressed by deconvolution and spatial filtering, i.e. using multi-channel linear prediction (MCLP) on the one hand and beamforming, e.g., a generalized sidelobe canceler (GSC), on the other hand. In this paper, in order to perform both deconvolution and spatial filtering, we propose to integrate MCLP and the GSC into a novel framework referred to as integrated sidelobe cancellation and linear prediction (ISCLP), wherein the sidelobe-cancellation (SC) filter and the linear prediction (LP) filter operate in parallel. Further, within this framework, we propose to estimate both filters jointly by means of a single Kalman filter. While ISCLP is roughly M times less expensive than a corresponding cascade of multiple-output MCLP and the GSC, where M denotes the number of microphones, it performs equally well in terms of dereverberation and noise reduction, as shown in simulations using one localized noise source.