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Wireless assistive listening devices (ALDs), such as induction loops, radio-frequency transmitters, and digital streaming systems, improve accessibility for people with hearing loss by transmitting from a venue’s sound system directly to the listener. Today, ALDs are used primarily for lectures and performances. When paired with advanced hearing devices, however, they could form part of an augmented listening system that lets users "remix" sounds in their environment, including from loudspeakers in public spaces. For example, users could amplify public announcements or suppress background music while having a conversation. In the proposed system, a binaural adaptive filter uses the ALD signal to estimate the loudspeaker sound at the ears. The hearing device can then either enhance or remove the loudspeaker sound in the hearing device output while preserving other nearby sounds. We demonstrate the proposed system using several commercial ALDs and assess the effects of delay, bandwidth, distortion, and noise on real-world system performance.
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AvA: An Adaptive Audio Filtering Architecture for Enhancing Mobile, Embedded, and Cyber-Physical Systems
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.
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- PAR ID:
- 10357211
- Date Published:
- Journal Name:
- 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
- Page Range / eLocation ID:
- 118 to 131
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPSN54338.2022.00017
