Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing

TL;DR

This paper introduces a fast, resource-efficient source separation method using Winner-Take-All hash codes for nearest neighbor search, enabling accurate single-channel audio denoising without complex models.

Contribution

The paper presents a novel iteration-free source separation algorithm based on WTA hashing, offering a faster and hardware-friendly alternative to traditional machine learning approaches.

Findings

WTA hash codes effectively discriminate audio spectra.

The method achieves competitive denoising performance.

Efficient bitwise search enables hardware implementation.

Abstract

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.