On real-time multi-stage speech enhancement systems

Lingjun Meng, Jozef Coldenhoff, Paul Kendrick, Tijana Stojkovic, Andrew Harper, Kiril Ratmanski, Milos Cernak
Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS)
2023-12-19 00:00:00
Recently, multi-stage systems have stood out among deep learning-based speech enhancement methods. However, these systems are always high in complexity, requiring millions of parameters and powerful computational resources, which limits their application for real-time processing in low-power devices. Besides, the contribution of various influencing factors to the success of multi-stage systems remains unclear, which presents challenges to reduce the size of these systems. In this paper, we extensively investigate a lightweight two-stage network with only 560k total parameters. It consists of a Mel-scale magnitude masking model in the first stage and a complex spectrum mapping model in the second stage. We first provide a consolidated view of the roles of gain power factor, post-filter, and training labels for the Mel-scale masking model. Then, we explore several training schemes for the two-stage network and provide some insights into the superiority of the two-stage network. We show that the proposed two-stage network trained by an optimal scheme achieves a performance similar to a four times larger open source model DeepFilterNet2.
PDF: On real-time multi-stage speech enhancement systems.pdf
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