|Daniel 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|
The authors present an approach for simultaneously separating and localizingmultiple sound sources using recorded microphone data.
We present an approach for simultaneously separating and localizingmultiple sound sources using recorded microphone data. Inspired by topicmodels, our approach is based on a probabilistic model of inter-microphonephase differences, and poses separation and localization as a Bayesianinference problem. We assume sound activity is locally smooth across time,frequency, and location, and use the known position of the microphones toobtain a consistent separation. We compare the performance of our methodagainst existing algorithms on simulated anechoic voice data and find that itobtains high performance across a variety of input conditions.