# Semi-supervised source localization with deep generative modeling

arXiv: https://arxiv.org/abs/2005.13163 · HTML: https://mlsanity.com/paper/2005.13163/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Semi-supervised source localization in reverberant environments with deep generative modeling](https://mlsanity.com/paper/2101.10636/) (2021)
- [Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications](https://mlsanity.com/paper/2110.11802/) (2021)
- [DeepAoANet: Learning Angle of Arrival from Software Defined Radios with Deep Neural Networks](https://mlsanity.com/paper/2112.00695/) (2021)
- [Audio scene monitoring using redundant ad-hoc microphone array networks](https://mlsanity.com/paper/2103.01830/) (2021)
- [A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods](https://mlsanity.com/paper/2507.01143/) (2025)
- [Feature Aggregation in Joint Sound Classification and Localization Neural Networks](https://mlsanity.com/paper/2310.19063/) (2023)

## Abstract (arXiv)

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.