Deep Active Speech Cancellation with Mamba-Masking Network

• URL: http://arxiv.org/abs/2502.01185v2
• Date: Sun, 25 May 2025 12:26:37 GMT
• Title: Deep Active Speech Cancellation with Mamba-Masking Network
• Authors: Yehuda Mishaly, Lior Wolf, Eliya Nachmani,
• Abstract summary: We present a novel deep learning network for Active Speech Cancellation (ASC)<n>The proposed Mamba-Masking architecture introduces a masking mechanism that directly interacts with the encoded reference signal.<n> Experimental results demonstrate substantial performance gains, achieving up to 7.2dB improvement in ANC scenarios and 6.2dB in ASC.
• Score: 62.73250985838971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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