Single Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids
Updated 4 h ago · first seen 11 Sept 2026
paper_01M294FT21062J1BGCTB4C7J2P
- Published
- 11 Sept 2026
- T1 · 4 h ago
- arXiv
- 2603.02724
- T1 · 4 h ago
- Category
- cs.SD
- T1 · 4 h ago
Abstract
-cross Abstract: This paper presents a simulation-based approach to own voice detection (OVD) in hearing aids using a single microphone. While OVD can significantly improve user comfort and speech intelligibility, enabling reliable OVD with a single microphone is desirable for simplifying hardware and reducing power consumption in compact hearing devices. However, most existing solutions rely on multiple microphones or additional sensors, increasing device complexity and cost. To enable ML-based OVD without requiring costly transfer-function measurements, we propose a data augmentation strategy based on simulated acoustic transfer functions (ATFs) that expose the model to a wide range of spatial propagation conditions. A transformer-based classifier is trained using analytically generated ATFs and further fine-tuned using numerically simulated ATFs with increasing geometric realism. This hierarchical adaptation enables the model to refine its spatial understanding while maintaining generalization. Experimental results show 95.52% accuracy on simulated head-and-torso test data and 90.02% accuracy for one-second speech segments, demonstrating robustness to short durations. When evaluated on real-world hearing-aid recordings, few-shot fine-tuning using a small subset achieves 91% accuracy, demonstrating that limited real-world data can effectively adapt the simulation-trained model. These results highlight the potential of simulation-based training for enabling practical single-microphone OVD systems in hearing aids.
Authors 4
Mathuranathan Mayuravaani, W. Bastiaan Kleijn, Andrew Lensen, Charlotte S{\o}rensen
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
- Arxiv announce type
- replace
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
- arXiv id
- 2603.02724
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
- Categories
- cs.SD, cs.LG
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
- Primary category
- cs.SD
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
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T19
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4 h ago
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| -cross Abstract: This paper presents a simulation-based approach to own voice detection (OVD) in hearing aids using a single microphone. While OVD can significantly improve user comfort and speech intelligibility, enabling reliable OVD with a single microphone is desirable for simplifying hardware and reducing power consumption in compact hearing devices. However, most existing solutions rely on multiple microphones or additional sensors, increasing device complexity and cost. To enable ML-based OVD without requiring costly transfer-function measurements, we propose a data augmentation strategy based on simulated acoustic transfer functions (ATFs) that expose the model to a wide range of spatial propagation conditions. A transformer-based classifier is trained using analytically generated ATFs and further fine-tuned using numerically simulated ATFs with increasing geometric realism. This hierarchical adaptation enables the model to refine its spatial understanding while maintaining generalization. Experimental results show 95.52% accuracy on simulated head-and-torso test data and 90.02% accuracy for one-second speech segments, demonstrating robustness to short durations. When evaluated on real-world hearing-aid recordings, few-shot fine-tuning using a small subset achieves 91% accuracy, demonstrating that limited real-world data can effectively adapt the simulation-trained model. These results highlight the potential of simulation-based training for enabling practical single-microphone OVD systems in hearing aids. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
- New paperPaperSingle Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids
New paper: Single Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 2 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.