用新模型实现语音主动消除，比传统降噪效果更好。
Deep Active Speech Cancellation with Mamba-Masking Network
- 提出Mamba-Masking结构，直接处理参考语音信号生成反向信号
- 在语音场景下提升6.2dB，降噪场景下提升7.2dB，性能显著
- 适合需要高精度语音抑制的实时通信与耳机场景
我们提出一种新型深度学习网络用于主动语音消除（ASC），超越传统主动降噪（ANC）方法，能有效消除噪声和语音信号。所提出的Mamba-Masking架构引入一种直接作用于编码参考信号的掩码机制，可在快速变化、高频的语音条件下实现自适应且精确对齐的反向信号生成。同时，采用多频段分割策略进一步提升各频带间的相位对齐效果。此外，设计了一种优化驱动的损失函数，为反向信号生成提供接近最优的监督信号。实验结果表明，该方法在ANC场景下性能提升达7.2dB，在ASC场景下提升达6.2dB，显著优于现有方法。
原文摘要 · Abstract (English)
We present a novel deep learning network for Active Speech Cancellation (ASC), advancing beyond Active Noise Cancellation (ANC) methods by effectively canceling both noise and speech signals. The proposed Mamba-Masking architecture introduces a masking mechanism that directly interacts with the encoded reference signal, enabling adaptive and precisely aligned anti-signal generation-even under rapidly changing, high-frequency conditions, as commonly found in speech. Complementing this, a multi-band segmentation strategy further improves phase alignment across frequency bands. Additionally, we introduce an optimization-driven loss function that provides near-optimal supervisory signals for anti-signal generation. Experimental results demonstrate substantial performance gains, achieving up to 7.2dB improvement in ANC scenarios and 6.2dB in ASC, significantly outperforming existing methods.
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