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Updated: Aug 8, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Published on: October 24, 2012
Learnable spectral dimension compression mapping for full-band speech enhancement
Qinwen Hu1, Zhongshu Hou1, Kai Chen1
1Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093, China qinwen.hu@smail.nju.edu.cn, zhongshu.hou@smail.nju.edu.cn, chenkai@nju.edu.cn, lujing@nju.edu.cn.
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.
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