Learnable spectral dimension compression mapping for full-band speech enhancement

Abstract

The highly imbalanced power spectral density of full-band speech signals poses a significant challenge to full-band speech enhancement, and the commonly used spectral features that mimic the behavior of the human auditory system are not an optimal choice for full-band speech enhancement. In this paper, a learnable spectral dimension compression mapping is proposed to effectively compress the spectral feature along frequency, preserving high resolution in low frequencies while compressing information in high frequencies in a more flexible manner. Experimental results verify that the proposed method can be easily combined with different full-band speech enhancement models and achieve better performance.