End-to-end neuromorphic speech enhancement with PDM microphones <sup>*</sup>
Bibliographic record
Abstract
Abstract Enhancing speech in noisy environments is essential for applications like automatic speech recognition, hearing aids, and real-time voice interfaces, but remains challenging on low-power, always-on edge devices. Conventional systems rely on pulse code modulation (PCM) signals and artificial neural networks, both of which introduce significant preprocessing and computational overhead. In this work, we present PDMDNS, a novel end-to-end neuromorphic framework for real-time speech denoising that directly processes binary pulse density modulation (PDM) microphone output using a spiking neural network, entirely bypassing the conventional PDM-to-PCM conversion and preprocessing stages. PDMDNS simultaneously performs speech enhancement and signal format conversion, leveraging stateless spiking neurons to reduce computational cost while maintaining temporal modeling capabilities. Moreover, when evaluated on a dataset containing noisy signals with SNRs ranging from 20 dB to −5 dB, our system achieves an average improvement of +7 dB in SI-SNR and a +3% gain in STOI. Although this performance is slightly below the current state-of-the-art by less than 1 dB, PDMDNS requires only 33 M-Ops/s, which is nearly 3× fewer operations than the best-performing spiking models. While PDM signals require a trade-off between maximizing precision through high sampling rates and minimizing energy consumption with lower rates, PDMDNS demonstrates robust generalization across varying input sampling rates (−12.5% to +37.5%) without the need for retraining. This flexibility makes it a compelling solution for energy-efficient, low-latency speech processing in embedded and neuromorphic systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".