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arXiv · 2606.04221

Feasibility of Time-Domain DNN-Based Speech Enhancement on Embedded FPGA for Hearing Aids

Feyisayo Olalere·Umut Altin·Kiki van der Heijden·Marcel van Gerven
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Abstract

Hearing aids impose strict latency and power constraints that current DNN-based speech enhancement systems struggle to meet on embedded hardware. We characterize this gap by deploying both speech separation and denoising using the lightweight SuDoRM-RF++ architecture on the AMD-Xilinx Kria KV260, evaluated at FP32 and 16-bit fixed-point precision for each task. Across these configurations, first-sample latency tracks with on-chip parameter caching rather than arithmetic throughput, identifying data movement as the primary bottleneck. Precision reduction halves the model memory footprint without compromising objective speech quality. The fixed-point denoising accelerator achieves a first-sample latency of 9.7~ms, meeting the 10~ms clinical threshold, while speech separation reaches 16.0~ms. These measurements establish concrete resource requirements for embedded DNN-based speech enhancement and quantify the remaining gap to hearing aid deployment.

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Feyisayo Olalere, Umut Altin, Kiki van der Heijden, Marcel van Gerven. 2026-07-17. Feasibility of Time-Domain DNN-Based Speech Enhancement on Embedded FPGA for Hearing Aids. https://arxiv.org/abs/2606.04221

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