@inproceedings{e583e4ff8e90411eadf3976c649ad6fb,
title = "A hypothesis testing approach for real-time multichannel speech separation using time-frequency masks",
abstract = "We propose a new approach to time-frequency mask generation for real-time multichannel speech separation. Whereas conventional approaches select the strongest source in each time-frequency bin, we perform a binary hypothesis test to determine whether a target source is present or not. We derive a generalized likelihood ratio test and extend it to underdetermined mixtures by aggregating the outputs of several tests with different interference models. This approach is justified by the nonstationarity and time-frequency disjointedness of speech signals. This computationally simple method is suitable for real-time source separation in resource-constrained and latency-critical applications.",
author = "Corey, \{Ryan M.\} and Singer, \{Andrew C.\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings ; Conference date: 13-09-2016 Through 16-09-2016",
year = "2016",
month = nov,
day = "8",
doi = "10.1109/MLSP.2016.7738827",
language = "English",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Kostas Diamantaras and Aurelio Uncini and Palmieri, \{Francesco A. N.\} and Jan Larsen",
booktitle = "2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings",
}