Scaling IoT-based noise cancellation to multiple noise sources
A recent development integrating Internet-of-Things (IoT) sensing techniques with active noise cancellation (ANC) has demonstrated certain benefits over the conventional methods for ANC, including wideband cancellation without blocking the ear and non-causal adaptive filtering. These benefits, however, can only be observed in acoustic environments with a single noise source. This thesis presents a new design for an IoT-based active noise cancellation system that can effectively cancel multiple independent noise sources. By incorporating multiple reference microphone inputs, the new system can estimate the unique acoustic channels between different sources of noise and the listener. Through simulation and hardware experiments, this new design is evaluated and shown to achieve significant improvement in cancellation over the previous implementation of IoT-based ANC.
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign