Parallel processing of distributed beamforming and multichannel linear prediction for speech denoising and deverberation in wireless acoustic sensor networks

More and more smart home devices with microphones come into our life in these years; it is highly desirable to connect these microphones as wireless acoustic sensor networks (WASNs) so that these devices can be better controlled in an enclosure. For indoor applications, both environmental noise and room reverberation may severely degrade speech quality, and thus both of them need to be removed to improve users' experience. For this goal, this paper proposes a parallel processing framework of distributed beamforming and multichannel linear prediction (DB-BFMCLP), which consists of generalized sidelobe canceler and multichannel linear prediction for simultaneous speech dereverberation and noise reduction in WASNs. By sharing a common desired response vector, the proposed DB-BFMCLP can provide a significant reduction in communication bandwidth without sacrificing performance. The convergence guarantee of the DB-BFMCLP to its centralized implementation is derived mathematically. Simulation results verify the superiority of the proposed method to the existing related methods in noisy and reverberant scenarios.