Informed source separation through spectrogram coding and data embedding

Abstract

We address the issue of underdetermined source separation in a particular informed configuration where both the sources and the mixtures are known during a so-called encoding stage. This knowledge enables the computation of a side-information which is small enough to be inaudibly embedded into the mixtures. At the decoding stage, the sources are no longer assumed to be known, only the mixtures and the extracted side-information are processed for source separation. The proposed system models the sources as independent and locally stationary Gaussian processes (GP) and the mixing process as a linear filtering. This model allows reliable estimation of the sources through generalized Wiener filtering, provided their spectrograms are known. As these spectrograms are too large to be embedded in the mixtures, we show how they can be efficiently approximated using either Nonnegative Tensor Factorization (NTF) or image compression. A high-capacity embedding method is used by the system to inaudibly embed the separation side-information into the mixtures. This method is an application of the Quantization Index Modulation technique applied to the time–frequency coefficients of the mixtures and permits to reach embedding rates of about 250kbps. Finally, a study of the performance of the full system is presented.