Poster
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments
Daniel D. Johnson · Daniel Gorelik · Ross E Mawhorter · Kyle Suver · Weiqing Gu · Steven Xing · Cody Gabriel · Peter Sankhagowit
Room 517 AB #112
Keywords: [ Source Separation ] [ Audio and Speech Processing ] [ Signal Processing ] [ Denoising ] [ Latent Variable Models ] [ Gaussian Processes ] [ Graphical Models ]
We present an approach for simultaneously separating and localizing multiple sound sources using recorded microphone data. Inspired by topic models, our approach is based on a probabilistic model of inter-microphone phase differences, and poses separation and localization as a Bayesian inference problem. We assume sound activity is locally smooth across time, frequency, and location, and use the known position of the microphones to obtain a consistent separation. We compare the performance of our method against existing algorithms on simulated anechoic voice data and find that it obtains high performance across a variety of input conditions.
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