1 research outputs found
Independent Low-Rank Matrix Analysis Based on Complex Student's -Distribution for Blind Audio Source Separation
In this paper, we generalize a source generative model in a state-of-the-art
blind source separation (BSS), independent low-rank matrix analysis (ILRMA).
ILRMA is a unified method of frequency-domain independent component analysis
and nonnegative matrix factorization and can provide better performance for
audio BSS tasks. To further improve the performance and stability of the
separation, we introduce an isotropic complex Student's -distribution as a
source generative model, which includes the isotropic complex Gaussian
distribution used in conventional ILRMA. Experiments are conducted using both
music and speech BSS tasks, and the results show the validity of the proposed
method.Comment: Preprint manuscript of 2017 IEEE International Workshop on Machine
Learning for Signal Processin