Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression

TL;DR

This paper proposes a dual-path compression method combining time and frequency domain techniques to reduce computational costs in neural echo cancellation and noise suppression, maintaining high performance across various compression ratios.

Contribution

Introduces a novel time-frequency dual-path compression approach that allows flexible control of model complexity with minimal performance loss.

Findings

Achieves compression ratios from 4x to 32x with little model size increase.

Outperforms existing fast models like FullSubNet and DeepFilterNet.

Maintains competitive noise cancellation and echo suppression performance.

Abstract

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.