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arXiv · 2509.21382

Multi-Speaker DOA Estimation in Binaural Hearing Aids using Deep Learning and Speaker Count Fusion

Farnaz Jazaeri·Homayoun Kamkar-Parsi·François Grondin·Martin Bouchard
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Abstract

For extracting a target speaker voice, direction-of-arrival (DOA) estimation is crucial for binaural hearing aids operating in noisy, multi-speaker environments. Among the solutions developed for this task, a deep learning convolutional recurrent neural network (CRNN) model leveraging spectral phase differences and magnitude ratios between microphone signals is a popular option. In this paper, we explore adding source-count information for multi-sources DOA estimation. The use of dual-task training with joint multi-sources DOA estimation and source counting is first considered. We then consider using the source count as an auxiliary feature in a standalone DOA estimation system, where the number of active sources (0, 1, or 2+) is integrated into the CRNN architecture through early, mid, and late fusion strategies. Experiments using real binaural recordings are performed. Results show that the dual-task training does not improve DOA estimation performance, although it benefits source-count prediction. However, a ground-truth (oracle) source count used as an auxiliary feature significantly enhances standalone DOA estimation performance, with late fusion yielding up to 14% higher average F1-scores over the baseline CRNN. This highlights the potential of using source-count estimation for robust DOA estimation in binaural hearing aids.

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Farnaz Jazaeri, Homayoun Kamkar-Parsi, François Grondin, Martin Bouchard. 2025-09-23. Multi-Speaker DOA Estimation in Binaural Hearing Aids using Deep Learning and Speaker Count Fusion. https://arxiv.org/abs/2509.21382

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