Blind audio source counting and separation of anechoic mixtures using the multichannel complex NMF framework

In this paper, we address the tasks of audio source counting and separation for a stereo anechoic mixture of audio signals. This will be achieved in two stages. In the first stage, a novel approach is introduced for estimating the number of sources as well as the channel mixing coefficients. For this purpose, a 2-D spectrum is evaluated against both the phase and amplitude differences of the two channels. Hence, obtaining the peak locations of the spectrum yields the number of the sources and the corresponding channel coefficients. In the second stage, an extension of a single channel complex matrix factorization method to multichannel is developed to extract the individual source signals. We find primary estimates of the sources via binary masking and then apply the complex factorization to the complex spectrogram of each source. The obtained factors are then utilized as initial values in the complex multichannel factorization model. We also suggest a method for estimating the number of required components for modeling each source. The separation performance improvement over the conventional methods is investigated by calculating BSS evaluation metrics. The comparison is also carried out in terms of source counting and localization with the recently proposed DeMIX-Anechoic method.