The present paper exposes a new technique that aims at solving an ill-posed source separation problem encountered in stereo mixtures. The proposed method is realized in an encoder-decoder framework: On the encoder side, a set of spectral envelopes is extracted from the original tracks, which are known. These envelopes are passed on to the decoder in attachment to the stereo mixture, whereas the frequency resolution of the former is adapted to the critical bands, and their magnitude is logarithmically quantized. On the decoder side, the mixture signal is decomposed by time-frequency selective iterative spatial filtering guided by a source activity index, which is derived from the spectral envelope values. A comparison with a similar algorithm reveals that the novel approach yields a higher perceptual audio quality at a much lower data rate.

Abstract-In this work it is shown how a dynamic nonlinear time-variant operator, such as a dynamic range compressor, can be inverted using an explicit signal model. By knowing the model parameters that were used for compression one is able to recover the original uncompressed signal from a "broadcast" signal with high numerical accuracy and very low computational complexity. A compressor-decompressor scheme is worked out and described in detail. The approach is evaluated on real-world audio material with great success.Index Terms-Dynamic range compression, inversion, modelbased, reverse audio engineering.

In this work we address a reverse audio engineering problem, i.e. the separation of stereo tracks of professionally produced music recordings. More precisely, we apply a spatial filtering approach with a quadratic constraint using an explicit sourceimage-mixture model. The model parameters are "learned" from a given set of original stereo tracks, reduced in size and used afterwards to demix the desired tracks in best possible quality from a preexisting mixture. Our approach implicates a side-information rate of 10 kbps per source or channel and has a low computational complexity. The results obtained for the SiSEC 2013 dataset are intended to be used as reference for comparison with unpublished approaches.