A Neural Denoising Vocoder for Clean Waveform Generation from Noisy Mel-Spectrogram based on Amplitude and Phase Predictions

Autor:	Du, Hui-Peng, Lu, Ye-Xin, Ai, Yang, Ling, Zhen-Hua
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	This paper proposes a novel neural denoising vocoder that can generate clean speech waveforms from noisy mel-spectrograms. The proposed neural denoising vocoder consists of two components, i.e., a spectrum predictor and a enhancement module. The spectrum predictor first predicts the noisy amplitude and phase spectra from the input noisy mel-spectrogram, and subsequently the enhancement module recovers the clean amplitude and phase spectrum from noisy ones. Finally, clean speech waveforms are reconstructed through inverse short-time Fourier transform (iSTFT). All operations are performed at the frame-level spectral domain, with the APNet vocoder and MP-SENet speech enhancement model used as the backbones for the two components, respectively. Experimental results demonstrate that our proposed neural denoising vocoder achieves state-of-the-art performance compared to existing neural vocoders on the VoiceBank+DEMAND dataset. Additionally, despite the lack of phase information and partial amplitude information in the input mel-spectrogram, the proposed neural denoising vocoder still achieves comparable performance with the serveral advanced speech enhancement methods. Comment: Accepted by NCMMSC2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.12268 Zobrazit plný text záznamu View this record from Arxiv