AI & ML
Optimized Compressor Parameterization for Produced Music in Hearing Aids
arXiv eess.AS0 views
arXiv:2610.29613v1 Announce Type: new Abstract: Hearing aid compression is typically designed for speech, yet music demands different trade-offs due to its narrower dynamic range (DR) and impulsive transients. This paper investigates whether music fitting benefits from being signal-aware, both in mapping compression demand across signal-audiogram conditions and in tuning its parameters to these combinations rather than inheriting them from speech-derived prescriptions. Compression demand is characterized across eight audiometric profiles, three residual DR severities, and three produced music signals under linear gain. Signal dynamics emerge as a first-order factor: production-compressed music fits the residual DR of the mild-to-moderate profiles evaluated, while dynamically rich recordings challenge even moderate hearing loss. Per-signal optimization of single- and multi-band wide dynamic range compression is then applied to two profiles with the highest compression demand. This optimization improves the Hearing Aid Audio Quality Index over gain-optimized NAL-NL2 baselines while also improving audibility, indicating substantial headroom for music-specific fitting.
Source:https://arxiv.org/abs/2610.29613PreviousA Perch out of Water: Few-Shot Detection of Bryde's Whale Vocalisations in False BayNextGeometry-Conditioned Target Speaker Extraction based on spatial features incorporating acoustic propagation principles
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