arXiv:2603.02794v2 Announce Type: replace-cross Abstract: We present TVF (Time-Varying Filtering), an interpretable, low-latency speech enhancement model for real-time, on-device assistive hearing. A lightweight neural controller predicts, in real time, the coefficients of a differentiable cascade of 35 second-order IIR filters (biquads), so the model tracks non-stationary noise while keeping a fully interpretable processing chain: every spectral modification is an explicit, adjustable equalizer curve rather than an opaque `black-box' transform. Because the biquad cascade carries the signal pr
The continuous development in lightweight, efficient AI models is enabling advanced signal processing to run on edge devices, transitioning from research to practical applications.
This technology represents a significant step towards highly personalized and effective on-device assistive hearing, potentially improving the quality of life for millions with hearing impairments.
Assistive hearing devices can now offer real-time, interpretable, and highly adaptable noise reduction, moving beyond fixed or less sophisticated algorithms.
- · Hearing aid manufacturers
- · People with hearing impairments
- · Edge AI chip developers
- · Speech enhancement researchers
- · Manufacturers of generic, less effective noise reduction solutions
Wider adoption of highly effective and personalized assistive hearing devices.
Increased demand for specialized, low-power AI processing units optimized for audio tasks in consumer electronics.
The interpretable nature of the AI could lead to more rapid regulatory approval and greater user trust in AI-powered medical devices.
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Read at arXiv cs.LG