  We propose a semi-supervised localization approach based on deep generative
modeling with variational autoencoders (VAEs). 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 performing 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-SSL can outperform both SRP-PHAT and CNN in
label-limited scenarios.
