HC-APNet: Harmonic Compensation Auditory Perception Network for low-complexity speech enhancement

Abstract

Speech enhancement is critical for improving speech quality and intelligibility in a variety of noisy environments. While neural network-based methods have shown promising results in speech enhancement, they often suffer from performance degradation in scenarios with limited computational resources. This paper presents HC-APNet (Harmonic Compensation Auditory Perception Network), a novel lightweight approach tailored to exploit the perceptual capabilities of the human auditory system for efficient and effective speech enhancement, with a focus on harmonic compensation. Inspired by human auditory reception mechanisms, we first segment audio into subbands using an auditory filterbank for speech enhancement. The use of subbands helps to reduce the number of parameters and the computational load, while the use of an auditory filterbank effectively preserves high-quality speech enhancement. In addition, inspired by the perception of human auditory context, we have developed an auditory perception network to capture gain information for different subbands. Furthermore, considering that subband processing only applies gain to the spectral envelope, which may introduce harmonic distortion, we design a learnable multi-subband comb-filter inspired by human pitch frequency perception to mitigate harmonic distortion. Finally, our proposed HC-APNet model achieves competitive performance on the speech quality evaluation metric with significantly less computational and parameter resources compared to existing methods on the VCTK ＋ DEMAND and DNS Challenge datasets.