# Active Audio Cancellation with Multi-Band Mamba Network

A novel deep learning approach for Active Audio Cancellation (AAC) is presented, which surpasses traditional Active Noise Cancellation (ANC) by effectively canceling any audio signal, regardless of its spectral content. We propose, for the first time, a deep learning approach to AAC using a novel multi-band Mamba architecture. This architecture partitions input audio into multiple frequency bands, allowing for precise anti-signal generation and enhanced phase alignment across frequencies, thereby improving overall cancellation performance. Additionally, we introduce an optimization-driven loss function that provides near-optimal supervisory signals for anti-signal generation. Our experimental results demonstrate substantial improvements over existing methods, achieving up to 7.2dB gain in ANC scenarios and up to 6.2dB improvement in AAC for voice audio signals, outperforming existing methods.

# Model

## Active Noise Cancellation

Please use headphones, as it may be difficult to hear otherwise.

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, DeepAAC , 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.

Primary signal d(n) | DeepAAC | ARN | Deep ANC | FxLMS
--- | --- | --- | --- | ---

## Active Speech Cancellation

Please use headphones, as it may be difficult to hear otherwise.

The first audio column, labeled Primary signal d(n), represents the signal that a listener would hear without the application of any AAC algorithm. The subsequent column, DeepAAC , 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.

Primary signal d(n) | DeepAAC | ARN | Deep ANC | FxLMS
--- | --- | --- | --- | ---