Audio source separation with multiple microphones on time-frequency representations

Hiroshi Sawada, Harold H. Szu
2013 Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI  
This paper presents various source separation methods that utilize multiple microphones. We classify them into two classes. Methods that fall into the first class apply independent component analysis (ICA) or Gaussian mixture model (GMM) to frequency bin-wise observations, and then solve the permutation problem to reconstruct separated signals. The second type of method extends non-negative matrix factorization (NMF) to a multimicrophone situation, in which NMF bases are clustered according to
more » ... heir spatial properties. We have a unified understanding that all methods analyze a time-frequency representation with an additional microphone axis. Downloaded From: http://proceedings.spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms Proc. of SPIE Vol. 8750 875007-4 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms
doi:10.1117/12.2018632 fatcat:725lfo3lmrba7l6uca6gopfi3q