Deep Active Speech Cancellation with Multi-Band Mamba Network

We present a novel deep learning framework for Active Speech Cancellation (ASC), advancing beyond Active Noise Cancellation (ANC) methods by effectively canceling both noise and speech signals. The proposed Multi-Band Mamba architecture segments input audio into distinct frequency bands, enabling precise anti-signal generation and improved phase alignment across frequencies for superior cancellation performance. 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.

Model

The first audio column, labeled Primary signal d(n), represents the signal that a listener would hear without the application of any ANC algorithm. The subsequent column, DeepASC , presents the canceling signal e(n) generated by our proposed model in response to the input signal from the first column. The following columns, ARN, DeepANC, and FxLMS, display the results produced by the ARN, DeepANC, and FxLMS methods, respectively, for the same input signal.

The first audio column, labeled Primary signal d(n), represents the signal that a listener would hear without the application of any ASC algorithm. The subsequent column, DeepASC , presents the canceling signal e(n) generated by our proposed model in response to the input signal from the first column. The following columns, ARN, DeepANC, and FxLMS, display the results produced by the ARN, DeepANC, and FxLMS methods, respectively, for the same input signal.