Deep learning-based denoising streamed from mobile phones improves speech-in-noise understanding for hearing aid users
Peter Udo Diehl, Hannes Zilly, Felix Sattler, Yosef Singer, Kevin, Kepp, Mark Berry, Henning Hasemann, Marlene Zippel, M\"uge Kaya, Paul, Meyer-Rachner, Annett Pudszuhn, Veit M. Hofmann, Matthias Vormann, Elias, Sprengel

TL;DR
This paper introduces a real-time deep learning denoising system on mobile devices that significantly enhances speech-in-noise understanding for hearing aid users, streamlining audio processing and improving user satisfaction.
Contribution
It presents the first mobile device-implemented denoising system streamed directly to hearing aids, improving speech intelligibility and user ratings in noisy environments.
Findings
Subjective audio ratings increased by over 40%.
Speech reception thresholds improved by 1.6 dB SRT.
Enhanced user satisfaction and preference for denoised audio.
Abstract
The hearing loss of almost half a billion people is commonly treated with hearing aids. However, current hearing aids often do not work well in real-world noisy environments. We present a deep learning based denoising system that runs in real time on iPhone 7 and Samsung Galaxy S10 (25ms algorithmic latency). The denoised audio is streamed to the hearing aid, resulting in a total delay of around 75ms. In tests with hearing aid users having moderate to severe hearing loss, our denoising system improves audio across three tests: 1) listening for subjective audio ratings, 2) listening for objective speech intelligibility, and 3) live conversations in a noisy environment for subjective ratings. Subjective ratings increase by more than 40%, for both the listening test and the live conversation compared to a fitted hearing aid as a baseline. Speech reception thresholds, measuring speech…
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Taxonomy
TopicsSpeech and Audio Processing · Hearing Loss and Rehabilitation · Noise Effects and Management
