Single microphone source separation using high resolution signal reconstruction

We present a method for separating two speakers from a single microphone channel. The method exploits the fine structure of male and female speech and relies on a strong high frequency resolution model for the source signals. The algorithm is able to identify the correct combination of male and female speech that best explains an observation and is able to reconstruct the component signals, relying on prior knowledge to ´fill in´ regions that are masked by the other speaker. The two speaker single microphone source separation problem is one of the most challenging source separation scenarios and few quantitative results have been reported in the literature. We provide a test set based on the Aurora 2 data set and report performance numbers on a portion of this set. We achieve results of 6.59 dB average increase in SNR for female speakers and 5.51 dB for male speakers.