Nonnegative Tensor Factorization for Directional Blind Audio Source Separation
Machine Learning
2017-05-30 v2 Machine Learning
Abstract
We augment the nonnegative matrix factorization method for audio source separation with cues about directionality of sound propagation. This improves separation quality greatly and removes the need for training data, with only a twofold increase in run time. This is the first method which can exploit directional information from microphone arrays much smaller than the wavelength of sound, working both in simulation and in practice on millimeter-scale microphone arrays.
Keywords
Cite
@article{arxiv.1411.5010,
title = {Nonnegative Tensor Factorization for Directional Blind Audio Source Separation},
author = {Noah D. Stein},
journal= {arXiv preprint arXiv:1411.5010},
year = {2017}
}