We propose a semi-supervised localization approach based on deep generative modeling with variational autoencoders (VAE). Localization in reverberant environments remains a challenge, which machine learning (ML) has shown promise in addressing. Even with large data volumes, the number of labels available for supervised learning in reverberant environments is usually small. We address this issue by perform semi-supervised learning (SSL) with convolutional VAEs. The VAE is trained to generate the phase of relative transfer functions (RTFs), in parallel with a DOA classifier, on both labeled and unlabeled RTF samples. The VAE-SSL approach is compared with SRP-PHAT and fully-supervised CNNs. We find that VAE-SLL can outperform both SRP-PHAT and CNN in label-limited scenarios.
|Title of host publication||Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020|
|Publisher||IEEE Computer Society|
|State||Published - Sep 2020|
|Event||30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 - Virtual, Espoo, Finland|
Duration: 21 Sep 2020 → 24 Sep 2020
|Name||IEEE International Workshop on Machine Learning for Signal Processing, MLSP|
|Conference||30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020|
|Period||21/09/20 → 24/09/20|
Bibliographical noteFunding Information:
This work is supported by the Office of Naval Research, Grant No. N00014-11-1-0439 and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 871245.
© 2020 IEEE.
- Deep learning
- Generative modeling
- Semi-supervised learning
- Source localization