AvA: An Adaptive Audio Filtering Architecture for Enhancing Mobile, Embedded, and Cyber-Physical Systems
Stephen Xia, Xiaofan Jiang · 2022
Audio is valuable in many mobile, embedded, and cyber-physical systems. We propose AvA, an acoustic adaptive filtering architecture, configurable to a wide range of applications and systems. By incorporating AvA into their own systems, developers can select which sounds to enhance or filter out depending on their application needs. AvA accomplishes this by using a novel adaptive beamforming algorithm called content-informed adaptive beam-forming (CIBF), that directly uses detectors and sound models that developers have created for their own applications to enhance or filter out sounds. CIBF uses a novel three step approach to prop-agate gradients from a wide range of different model types and signal feature representations to learn filter coefficients. We apply AvA to four scenarios and demonstrate that AvA enhances their respective performances by up to 11.1%. We also integrate AvA into two different mobile/embedded platforms with widely different resource constraints and target sounds/noises to show the boosts in performance and robustness these applications can see using AvA.