提出轻量级语音增强模型，多麦克风设备压缩数据传输
CaSNet: Compress-and-Send Network Based Multi-Device Speech Enhancement Model for Distributed Microphone Arrays
- 设备端压缩原始音频，通过奇异值分解降维
- 融合中心对齐特征后解码，保持语音质量
- 显著降低带宽消耗，适合资源受限场景
分布式麦克风阵列（DMA）是下一代语音交互的有前景平台，但噪声环境下仍需语音增强（SE）。现有方法通常将所有设备的原始波形汇聚到融合中心（FC），再设计多麦克风模型，导致高带宽与能耗。本文提出一种面向资源受限DMA的压缩-发送网络（CaSNet），其中一麦克风作为FC和参考。其余设备将原始数据编码为特征矩阵，经奇异值分解（SVD）压缩为更紧凑表示。FC接收特征后，通过跨窗口查询对齐参考信号，再经神经解码生成空间一致的增强语音。多数据集实验表明，该方法可大幅减少数据量，性能损失微乎其微。代码已开源：https://github.com/Jokejiangv/CaSNet。
原文摘要 · Abstract (English)
Distributed microphone array (DMA) is a promising next-generation platform for speech interaction, where speech enhancement (SE) is still required to improve the speech quality in noisy cases. Existing SE methods usually first gather raw waveforms at a fusion center (FC) from all devices and then design a multi-microphone model, causing high bandwidth and energy costs. In this work, we propose a \emph{Compress-and-Send Network (CaSNet)} for resource-constrained DMAs, where one microphone serves as the FC and reference. Each of other devices encodes the measured raw data into a feature matrix, which is then compressed by singular value decomposition (SVD) to produce a more compact representation. The received features at the FC are aligned via cross window query with respect to the reference, followed by neural decoding to yield spatially coherent enhanced speech. Experiments on multiple datasets show that the proposed CaSNet can save the data amount with a negligible impact on the performance compared to the uncompressed case. The reproducible code is available at https://github.com/Jokejiangv/CaSNet.
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