Extension of Sparse, Adaptive Signal Decompositions to Semi-blind Audio Source Separation

Abstract. We apply sparse, fast and flexible adaptive lapped orthogonal transforms to underdetermined audio source separation using the time-frequency masking framework. This normally requires the sources to overlap as little as possible in the time-frequency plane. In this work, we apply our adaptive transform schemes to the semiblind case, in which the mixing system is already known, but the sources are unknown. By assuming that exactly two sources are active at each time-frequency index, we determine both the adaptive transforms and the estimated source coefficients using 1 norm minimisation. We show average performance of 12–13 dB SDR on speech and music mixtures, and show that the adaptive transform scheme offers improvements in the order of several tenths of a dB over transforms with constant block length. Comparison with previously studied upper bounds suggests that the potential for future improvements is significant.

• Type: Conference
• Year: 2009
• Where: IDA
• Authors: Andrew Nesbit, Emmanuel Vincent, Mark D. Plumbley
• Keywords: Adaptive Transform, Adaptive Transform Scheme, Artificial Intelligence, IDA 2009, Time-frequency Masking Framework