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arXiv · 2608.03650

Echo-Aware Modulation for Compact-Latent Frequency-Time Modeling in Lightweight Acoustic Echo Cancellation

Ye Ni·Ruiyu Liang·Qingyun Wang·Kai Xie·Cairong Zou·Björn W. Schuller
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Abstract

Existing lightweight acoustic echo cancellation (AEC) systems often combine linear AEC with Bark-domain DNN-based suppression to lower the computational footprint. In such systems, downsampling layers further compress the input features into a compact bottleneck representation, but this compression weakens frequency-time modeling capacity and degrades performance. To mitigate this limitation, we propose MSA-EchoLite, a lightweight Bark-domain AEC framework with an asymmetric dual-branch encoder and an echo-aware frequency-time modulation (EAM) module. The EAM module enriches the compressed bottleneck representation by modeling discrepancy and correlation cues between the dual-branch microphone and echo-related latent features. Experimental results show that the Bark-domain variant of MSA-EchoLite offers a better performance-complexity trade-off than its frequency-domain counterpart but is more sensitive to feature compression. With only 26.1% additional FLOPs over its non-EAM Bark-domain variant, its EAM-enhanced version achieves 99.1% of the PESQ of the frequency-domain counterpart, which requires nearly twice the FLOPs, and even surpasses it in SDR. Overall, MSA-EchoLite outperforms state-of-the-art lightweight AEC models while using only 0.2 M parameters and 100 M FLOPs/s.

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BibTeXRIS

Ye Ni, Ruiyu Liang, Qingyun Wang, Kai Xie, Cairong Zou, Björn W. Schuller. 2026-08-04. Echo-Aware Modulation for Compact-Latent Frequency-Time Modeling in Lightweight Acoustic Echo Cancellation. https://arxiv.org/abs/2608.03650

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