McNet: Fuse Multiple Cues for Multichannel Speech Enhancement
This addresses speech enhancement for audio processing applications, but it is incremental as it builds on existing methods with a novel fusion approach.
The paper tackled the problem of fully exploiting spectral and spatial information with temporal dynamics for multichannel speech enhancement by proposing McNet, a multi-cue fusion network that cascades four modules; experiments showed it notably outperforms other state-of-the-art methods.
In multichannel speech enhancement, both spectral and spatial information are vital for discriminating between speech and noise. How to fully exploit these two types of information and their temporal dynamics remains an interesting research problem. As a solution to this problem, this paper proposes a multi-cue fusion network named McNet, which cascades four modules to respectively exploit the full-band spatial, narrow-band spatial, sub-band spectral, and full-band spectral information. Experiments show that each module in the proposed network has its unique contribution and, as a whole, notably outperforms other state-of-the-art methods.