Single mixture audio sources separation using ISA technique in EMD domain

This paper introduces a novel technique that is developed to separate the audio sources from a single mixture. Indeed, audio signals and, in particular, musical signals can be well approximated by a sum of damped sinusoidal (modal) components. Based on this representation, Empirical Mode Decomposition (EMD) is employed to extract Intrinsic Mode Functions (IMFs) for audio mixture signal. By applying PCA (Principal Component Analysis) to the extracted components, we find uncorrelated components which are the artificial observations. Then we obtain independent components by applying Independent Component Analysis (ICA) to the uncorrelated components. A k-means clustering algorithm is introduced to group the independent basis vectors into the number of component sources inside the mixture.