  We propose an iteration-free source separation algorithm based on
Winner-Take-All (WTA) hash codes, which is a faster, yet accurate alternative
to a complex machine learning model for single-channel source separation in a
resource-constrained environment. We first generate random permutations with
WTA hashing to encode the shape of the multidimensional audio spectrum to a
reduced bitstring representation. A nearest neighbor search on the hash codes
of an incoming noisy spectrum as the query string results in the closest
matches among the hashed mixture spectra. Using the indices of the matching
frames, we obtain the corresponding ideal binary mask vectors for denoising.
Since both the training data and the search operation are bitwise, the
procedure can be done efficiently in hardware implementations. Experimental
results show that the WTA hash codes are discriminant and provide an affordable
dictionary search mechanism that leads to a competent performance compared to a
comprehensive model and oracle masking.
