The DUET and DESPRIT blind source separation algorithms attempt to recover J sources from I mixtures of these sources, in the interesting case where J > I, with minimal information about the mixing environment of underling sources statistics. We present a semi-blind generalization of the DUET-DESCRIPT approach which allows arbitary placement of the sensors and demixes the sources given the room impulse response. We learn a sparse representation of the mixtures on an over-complete spatial signatures dictionary. We localize and separate the constituent sources via binary masking of a power weighted histogram in location space or in attenuation-delay space. We demonstrate the robustness of this technique using synthetic room experiments.
|Publication status||Published - 2008|
|Event||8th International Conference on Mathematics in Signal Processing - IMA (The Institute of Mathematics and its Applications, 2008), Cirencester, UK, IMA (The Institute of Mathematics and its Applications, 2008), Cirencester, UK|
Duration: 01 Jan 2008 → …
|Conference||8th International Conference on Mathematics in Signal Processing|
|City||IMA (The Institute of Mathematics and its Applications, 2008), Cirencester, UK|
|Period||01/01/2008 → …|
- Blind Source Separation, Time Frequency Analysis, Localization; Fourier Analysis; Short Time Fourier Transform; Dictionary Learning.