Underdetermined source separation using a sparse STFT framework and weighted laplacian directional modelling
This incremental improvement addresses audio source separation for scenarios with limited sensors, such as in audio processing or telecommunications.
The paper tackled the underdetermined audio source separation problem with more sources than sensors by proposing a method using weighted mixtures of Generalized Directional Laplacian Densities in a sparse STFT domain, which improved results over previous work and performed competitively with state-of-the-art methods.
The instantaneous underdetermined audio source separation problem of K-sensors, L-sources mixing scenario (where K < L) has been addressed by many different approaches, provided the sources remain quite distinct in the virtual positioning space spanned by the sensors. This problem can be tackled as a directional clustering problem along the source position angles in the mixture. The use of Generalised Directional Laplacian Densities (DLD) in the MDCT domain for underdetermined source separation has been proposed before. Here, we derive weighted mixtures of DLDs in a sparser representation of the data in the STFT domain to perform separation. The proposed approach yields improved results compared to our previous offering and compares favourably with the state-of-the-art.