Learnable spectral dimension compression mapping for full-band speech enhancement

A unified beamforming and source separation model for static and dynamic human-robot interaction

This paper presents a unified model for combining beamforming and blind source separation (BSS). The validity of the model's assumptions is confirmed by recovering target speech information in noise accurately using Oracle information. Using real static human-robot interaction (HRI) data, the proposed combination of BSS with the minimum-variance distortionless response beamformer provides a greater signal-to-noise ratio (SNR) than previous parallel and cascade systems that combine BSS and beamforming. In the difficult-to-model HRI dynamic environment, the system provides a SNR gain that was 2.8 dB greater than the results obtained with the cascade combination, where the parallel combination is infeasible.

Local spectral attention for full-band speech enhancement

Attention mechanism has been widely used in speech enhancement (SE) because, theoretically, it can effectively model the inherent connection of signal in time domain and spectrum domain. In this Letter, it is found that the attention over the entire frequency range hampers the inference for full-band SE and possibly leads to excessive residual noise and degradation of speech. To alleviate this problem, the local spectral attention is introduced into full-band SE model by limiting the span of attention. The ablation tests on three full-band SE models reveal that the local frequency attention can effectively improve overall performance.