Nonnegative Tensor Factorization for Directional Blind Audio Source Separation
This addresses the challenge of separating audio sources using small microphone arrays, which is incremental as it builds on existing methods.
The paper tackles the problem of blind audio source separation by augmenting nonnegative matrix factorization with directional cues, resulting in greatly improved separation quality and eliminating the need for training data, with only a twofold increase in run time.
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.