@inproceedings{d3c6ead6e4ae4eefbe975509f1e2032f,
title = "Semi-supervised source localization with deep generative modeling",
abstract = "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.",
keywords = "Deep learning, Generative modeling, Semi-supervised learning, Source localization",
author = "Bianco, {Michael J.} and Sharon Gannot and Peter Gerstoft",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 ; Conference date: 21-09-2020 Through 24-09-2020",
year = "2020",
month = sep,
doi = "10.1109/MLSP49062.2020.9231825",
language = "الإنجليزيّة",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020",
address = "الولايات المتّحدة",
}