A VQ-Based Single-Channel Audio Separation for Music/Speech Mixtures

In this paper, we address the problem of audio source separation with one single sensor, based on estimation of statistical model of the sources. We improve the-state-of the art Vector Quantization (VQ) by considering apriori histograms of huge training data. This will result in a more accurate codebook for each source in contrast to the commonly used Linde-Buzo-Gray (LBG) algorithm. An optimum estimator is introduced in separation stage based on Discrete Fourier Transform (DFT) amplitudes. Finally, conducting different simulations it is demonstrated that proposed approach efficiently segregated audio mixtures in terms of Signal to Distortion Ratio (SDR) measures as well as Mean Opinion Score (MOS) criterion.