Deep Learning Based Audio-Visual Multi-Speaker DOA Estimation Using Permutation-Free Loss Function

Qing Wang; Hang Chen; Ya Jiang; Zhe Wang; Yuyang Wang; Jun Du; Chin-Hui Lee

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arXiv · 2210.14581

Deep Learning Based Audio-Visual Multi-Speaker DOA Estimation Using Permutation-Free Loss Function

Qing Wang·Hang Chen·Ya Jiang·Zhe Wang·Yuyang Wang·Jun Du·Chin-Hui Lee

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Abstract

In this paper, we propose a deep learning based multi-speaker direction of arrival (DOA) estimation with audio and visual signals by using permutation-free loss function. We first collect a data set for multi-modal sound source localization (SSL) where both audio and visual signals are recorded in real-life home TV scenarios. Then we propose a novel spatial annotation method to produce the ground truth of DOA for each speaker with the video data by transformation between camera coordinate and pixel coordinate according to the pin-hole camera model. With spatial location information served as another input along with acoustic feature, multi-speaker DOA estimation could be solved as a classification task of active speaker detection. Label permutation problem in multi-speaker related tasks will be addressed since the locations of each speaker are used as input. Experiments conducted on both simulated data and real data show that the proposed audio-visual DOA estimation model outperforms audio-only DOA estimation model by a large margin.

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BibTeX RIS

Qing Wang, Hang Chen, Ya Jiang, Zhe Wang, Yuyang Wang, Jun Du, Chin-Hui Lee. 2022-10-26. Deep Learning Based Audio-Visual Multi-Speaker DOA Estimation Using Permutation-Free Loss Function. https://arxiv.org/abs/2210.14581

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