  We propose a novel sparse representation for heavily underdetermined
multichannel sound mixtures, i.e., with much more sources than microphones. The
proposed approach operates in the complex Fourier domain, thus preserving
spatial characteristics carried by phase differences. We derive a
generalization of K-SVD which jointly estimates a dictionary capturing both
spectral and spatial features, a sparse activation matrix, and all
instantaneous source phases from a set of signal examples. The dictionary can
then be used to extract the learned signal from a new input mixture. The method
is applied to the challenging problem of ego-noise reduction for robot
audition. We demonstrate its superiority relative to conventional
dictionary-based techniques using recordings made in a real room.
