Signal Processing and Deep Learning for Practical Active Noise Control

The presentation delves into the practicalities of engineering ANC systems for both headphones and windows, showcasing some prototype systems from the Digital Signal Processing Lab at Nanyang Technological University, Singapore. The talk illustrates how machine learning and digital signal processing techniques have been adeptly harnessed to identify noise patterns and masterfully mitigate noise. This discussion will include insights into the design of ANC systems based on theoretical studies that ensure stability and robustness, enabling the deployment of autonomous ANC systems in real-world settings. Furthermore, this speech introduces an innovative approach to ANC design that departs from the traditional method of employing deep neural networks directly in the secondary path for anti-noise generation—a method often too complex and impractical for real-world application. Our approach centers on a delayless method in selective[2] or generative[3] control filters that can effectively manage in-situ noise conditions. The speaker elucidates this novel DNN-driven methodology and provide perspectives on how this rapid-response strategy can address dynamic noise variations and the associated changes in noise paths and characteristics.