SMRU: Split-and-Merge Recurrent-based UNet for Acoustic Echo Cancellation and Noise Suppression

TL;DR

This paper introduces SMRU, a versatile recurrent UNet model with multi-scale band processing for acoustic echo cancellation and noise suppression, optimized for deployment across diverse scenarios including edge and cloud environments.

Contribution

The paper proposes a novel recurrent UNet architecture with multi-scale band split and merge layers, enhancing adaptability and efficiency for various acoustic processing scenarios.

Findings

Achieves competitive or superior performance compared to existing methods.

Configurable from low to high complexity, suitable for different deployment environments.

Demonstrates effective multi-resolution feature modeling with variable frame rate blocks.

Abstract

The proliferation of deep neural networks has spawned the rapid development of acoustic echo cancellation and noise suppression, and plenty of prior arts have been proposed, which yield promising performance. Nevertheless, they rarely consider the deployment generality in different processing scenarios, such as edge devices, and cloud processing. To this end, this paper proposes a general model, termed SMRU, to cover different application scenarios. The novelty lies in two-fold. First, a multi-scale band split layer and band merge layer are proposed to effectively fuse local frequency bands for lower complexity modeling. Besides, by simulating the multi-resolution feature modeling characteristic of the classical UNet structure, a novel recurrent-dominated UNet is devised. It consists of multiple variable frame rate blocks, each of which involves the causal time down-/up-sampling layer with varying compression ratios and the dual-path structure for inter- and intra-band modeling. The model is configured from 50 M/s to 6.8 G/s in terms of MACs, and the experimental results show that the proposed approach yields competitive or even better performance over existing baselines, and has the full potential to adapt to more general scenarios with varying complexity requirements.