Blind Separation of Speech Mixtures via Time-Frequency Masking

TLDR

Binary time‑frequency masks enable source separation when the sources’ time‑frequency representations do not overlap, a condition termed W‑disjoint orthogonality. The study introduces approximate W‑disjoint orthogonality and demonstrates that ideal binary masks can be approximated when two anechoic mixtures are available. A power‑weighted two‑dimensional histogram of the ratio of time‑frequency representations is used to locate each source’s attenuation and delay, enabling construction of masks that separate the mixtures. Experiments confirm that ideal binary masks exist for separating multiple speech signals and that the proposed histogram‑based method successfully approximates these masks. Demixing results are available online at http://alum.mit.edu/www/rickard/bss.html.

Abstract

Binary time-frequency masks are powerful tools for the separation of sources from a single mixture. Perfect demixing via binary time-frequency masks is possible provided the time-frequency representations of the sources do not overlap: a condition we call W-disjoint orthogonality. We introduce here the concept of approximate W-disjoint orthogonality and present experimental results demonstrating the level of approximate W-disjoint orthogonality of speech in mixtures of various orders. The results demonstrate that there exist ideal binary time-frequency masks that can separate several speech signals from one mixture. While determining these masks blindly from just one mixture is an open problem, we show that we can approximate the ideal masks in the case where two anechoic mixtures are provided. Motivated by the maximum likelihood mixing parameter estimators, we define a power weighted two-dimensional (2-D) histogram constructed from the ratio of the time-frequency representations of the mixtures that is shown to have one peak for each source with peak location corresponding to the relative attenuation and delay mixing parameters. The histogram is used to create time-frequency masks that partition one of the mixtures into the original sources. Experimental results on speech mixtures verify the technique. Example demixing results can be found online at http://alum.mit.edu/www/rickard/bss.html.