Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing
This addresses efficient audio source separation for low-resource applications, but it is incremental as it builds on existing hashing and nearest neighbor techniques.
The paper tackles single-channel source separation in resource-constrained environments by proposing an iteration-free algorithm using Winner-Take-All hashing for bitwise nearest neighbor search, achieving competent performance compared to a comprehensive model and oracle masking.
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.