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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

Tue Dec 04 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #112
in Tue Poster Session B »

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.

Keywords: Graphical Models · Gaussian Processes · Latent Variable Models · Denoising · Signal Processing · Audio and Speech Processing · Source Separation
[ Paper]  [ Poster] 

Author Information

Daniel D. Johnson (Harvey Mudd College)
Daniel Gorelik (Harvey Mudd College)
Ross E Mawhorter (Harvey Mudd College)
Kyle Suver (Harvey Mudd College)
Weiqing Gu (Harvey Mudd College)
Steven Xing (Intel Corporation)
Cody Gabriel (Intel Corporation)
Peter Sankhagowit (Intel Corporation)

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