Adaptation of Multiple Sound Source Localization Neural Networks with Weak Supervision and Domain-Adversarial Training
| Type of publication: | Conference paper |
| Citation: | He_ICASSP_2019 |
| Booktitle: | Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
| Year: | 2019 |
| Month: | May |
| Pages: | 770-774 |
| Location: | Brighton, United Kingdom |
| ISSN: | 2379-190X |
| ISBN: | 978-1-4799-8131-1 |
| Crossref: | He_Idiap-Com-01-2019: |
| DOI: | 10.1109/ICASSP.2019.8682655 |
| Abstract: | Despite the recent success of deep neural network-based approaches in sound source localization, these approaches suffer the limitations that the required annotation process is costly, and the mismatch between the training and test conditions undermines the performance. This paper addresses the question of how models trained with simulation can be exploited for multiple sound source localization in real scenarios by domain adaptation. In particular, two domain adaptation methods are investigated: weak supervision and domain-adversarial training. Our experiments show that the weak supervision with the knowledge of the number of sources can significantly improve the performance of an unadapted model. However, the domain-adversarial training does not yield significant improvement for this particular problem. |
| Keywords: | direction-of-arrival estimation, DOA estimation, domain adaptation, Encoding, Feature extraction, neural networks, Position measurement, Robots, sound source localization, training, weakly-supervised learning. |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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