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This paper proposes an efficient bitwise solution to the single-channelsource separation task. Most dictionary-based source separation algorithms relyon iterative update rules during the run time, which becomes computationallycostly especially when we employ an overcomplete dictionary and sparse encodingthat tend to give better separation results. To avoid such cost we propose abitwise scheme on hashed spectra that leads to an efficient posteriorprobability calculation. For each source, the algorithm uses a partial rankorder metric to extract robust features that form a binarized dictionary ofhashed spectra. Then, for a mixture spectrum, its hash code is compared witheach source's hashed dictionary in one pass. This simple voting-baseddictionary search allows a fast and iteration-free estimation of ratio maskingat each bin of a signal spectrogram. We verify that the proposed BitWise SourceSeparation (BWSS) algorithm produces sensible source separation results for thesingle-channel speech denoising task, with 6-8 dB mean SDR. To our knowledge,this is the first dictionary based algorithm for this task that is completelyiteration-free in both training and testing.

Bitwise Source Separation on Hashed Spectra: An Efficient Posterior Estimation Scheme Using Partial Rank Order Metrics