LCSM: A Lightweight Complex Spectral Mapping Framework for Stereophonic Acoustic Echo Cancellation
Chenggang Zhang, Jinjiang Liu, Xueliang Zhang

TL;DR
This paper introduces LCSM, a lightweight deep learning framework for stereophonic acoustic echo cancellation that outperforms previous methods while maintaining low computational complexity, suitable for resource-limited devices.
Contribution
The paper proposes a novel lightweight complex spectral mapping framework with a multi-input multi-output scheme and a cross-domain loss for end-to-end SAEC without decorrelation preprocessing.
Findings
LCSM significantly outperforms previous methods in various conditions.
The model has only 0.55 million parameters, making it suitable for resource-limited devices.
The framework demonstrates strong generalization capabilities.
Abstract
The traditional adaptive algorithms will face the non-uniqueness problem when dealing with stereophonic acoustic echo cancellation (SAEC). In this paper, we first propose an efficient multi-input and multi-output (MIMO) scheme based on deep learning to filter out echoes from all microphone signals at once. Then, we employ a lightweight complex spectral mapping framework (LCSM) for end-to-end SAEC without decorrelation preprocessing to the loudspeaker signals. Inplace convolution and channel-wise spatial modeling are utilized to ensure the near-end signal information is preserved. Finally, a cross-domain loss function is designed for better generalization capability. Experiments are evaluated on a variety of untrained conditions and results demonstrate that the LCSM significantly outperforms previous methods. Moreover, the proposed causal framework only has 0.55 million parameters, much…
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Taxonomy
TopicsSpeech and Audio Processing · Advanced Adaptive Filtering Techniques · Acoustic Wave Phenomena Research
