Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression
This work addresses the need for efficient and tunable models in full-duplex communication, though it appears incremental as it builds on existing compression techniques.
The paper tackles the problem of high computational cost and inflexibility in neural networks for joint echo cancellation and noise suppression by introducing time-frequency dual-path compression, achieving compression ratios from 4x to 32x with minimal model size change and competitive performance against existing models like fast FullSubNet and DeepFilterNet.
Echo cancellation and noise reduction are essential for full-duplex communication, yet most existing neural networks have high computational costs and are inflexible in tuning model complexity. In this paper, we introduce time-frequency dual-path compression to achieve a wide range of compression ratios on computational cost. Specifically, for frequency compression, trainable filters are used to replace manually designed filters for dimension reduction. For time compression, only using frame skipped prediction causes large performance degradation, which can be alleviated by a post-processing network with full sequence modeling. We have found that under fixed compression ratios, dual-path compression combining both the time and frequency methods will give further performance improvement, covering compression ratios from 4x to 32x with little model size change. Moreover, the proposed models show competitive performance compared with fast FullSubNet and DeepFilterNet.