Biophysically-inspired single-channel speech enhancement in the time domain

Abstract

Most state-of-the-art speech enhancement (SE) methods utilize time-frequency (T-F) features or waveforms as input features and have poor generalizability at negative signal-to-noise ratios (SNR). To overcome these issues, we propose a novel network that integrates biophysical properties of the human auditory system known to perform even at negative SNRs. We generated biophysical features using CoNNear, a neural network auditory model, which were fed into a SOTA speech enhancement model AECNN. The model was trained on the INTER-SPEECH 2021 DNS Challenge dataset and evaluated on mismatched noise conditions at various SNRs. The experimental results revealed that the bio-inspired approaches outperformed T-F and waveform features under positive SNRs and demonstrated stronger robustness to unseen noise at negative SNRs. We conclude that incorporating human-like features can extend the operating range of SE systems to more negative SNRs.