Subband-based Spectrogram Fusion for Speech Enhancement by Combining Mapping and Masking Approaches
Hao Shi, Longbiao Wang, Sheng Li, Jianwu Dang, Tatsuya Kawahara · 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · 2022
Deep learning brings effective optimization and sig-nificant improvements to speech enhancement (SE). Mapping and masking are currently major approaches in single-channel frequency-domain SE with supervised learning. In this work, we first show that these two approaches are complementary in that mapping is more effective in low-frequency bands, while masking is more suitable in high-frequency bands. This is because the high-frequency bands typically have low energy, so estimating the enhanced spectrogram directly does not make sense. Moreover, learning on the low-energy parts is often annihilated by learning on the high-energy parts during the entire loss calculation. To exploit this complementarity, we propose subband-based spectrogram fusion (SBSF), which combines the spectrogram of low-frequency and high-frequency estimated by different SE models. Experimental evaluations show that the SBSF significantly improved the SE performance.