The underdetermined blind audio source separation problem is often addressed in the time-frequency domain by assuming that each time-frequency point is an independently distributed random variable. Other approaches which are not blind assume a more structured model, like the Spectral Gaussian Mixture Models (Spectral-GMMs), thus exploiting statistical diversity of audio sources in the separation process. However, in this last approach, Spectral-GMMs are supposed to be learned from some training signals. In this paper, we propose a new approach for learning Spectral-GMMs of the sources without the need of using training signals. The proposed blind method significantly outperforms state-of-the-art approaches on stereophonic instantaneous music mixtures. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Arberet, S., Ozerov, A., Gribonval, R., & Bimbot, F. (2009). Blind spectral-GMM estimation for underdetermined instantaneous audio source separation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5441, pp. 751–758). https://doi.org/10.1007/978-3-642-00599-2_94
Mendeley helps you to discover research relevant for your work.