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Semi-supervised Source Localization in Reverberant Environments with Deep Generative Modeling by Bianco, Michael J.; Gannot, Sharon; Fernandez-Grande, Efren; Gerstoft, Peter is a scholarly article available to read on EtoBox.

What is Semi-supervised Source Localization in Reverberant Environments with Deep Generative Modeling about?

We propose a semi-supervised approach to acoustic source localization in reverberant environments based on deep generative modeling. Localization in reverberant environments remains an open challenge. 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 variational autoencoders (VAEs) on reverberant speech signals recorded with microphone arrays. The VAE is trained to generate the phase of relative transfer functions (RTFs) between microphones, in parallel with a direction of arrival (DOA) classifier based on RTF-phase. These models are trained using both labeled and unlabeled RTF-phase sequences. In learning to perform these tasks, the VAE-SSL explicitly learns to separate the physical causes of the RTF-phase (i.e., source location) from distracting signal characteristics such as noise and speech activity. Relative to existing semi-supervised localization methods in acoustics, VAE-SSL is effectively an end-to-end processing approach which relies on minimal preprocessing of RTF-phase features. As far as we are aware, our

Author

Bianco, Michael J.; Gannot, Sharon; Fernandez-Grande, Efren; Gerstoft, Peter

Published

2021

Language

EN