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arXiv Machine Learning By Mathuranathan Mayuravaani, W. Bastiaan Kleijn, Andrew Lensen, Charlotte S{\o}rensen

Single Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids

Read the original on arXiv Machine Learning →

The paper introduces a simulation-based method for detecting a user's own voice in hearing aids using only a single microphone. It employs a data augmentation strategy with simulated acoustic transfer functions to train a transformer classifier, achieving over 90% accuracy on both simulated and real-world recordings. The approach reduces hardware complexity and power consumption while maintaining robust performance across varied spatial conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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