Vocal separation using nearest neighbours and median filtering

Recently, single channel vocal separation algorithms have been proposed which exploit the fact that most popular music can be regarded as a repeating musical background over which a locally non-repeating vocal signal is superimposed. In this paper we describe a novel vocal separator inspired by these approaches which finds the k nearest neighbours to each frame of a spectrogram of the mixture signal. The median value of these frames is then used as the estimate of the background music at the current frame. This is then used to generate a mask on the original complex-valued spectrogram before inversion to the time domain. The effectiveness of the approach is demonstrated on a number of real-world signals.