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.
Please use headphones, as it may be difficult to hear otherwise.
The first audio column, labeled
Primary signal d(n) | DeepAAC | ARN | Deep ANC | FxLMS |
---|---|---|---|---|
Please use headphones, as it may be difficult to hear otherwise.
The first audio column, labeled
Primary signal d(n) | DeepAAC | ARN | Deep ANC | FxLMS |
---|---|---|---|---|