REAL-TIME BINAURAL BLIND SOURCE SEPARATION
Changkyu Choi · 2003
Binaural blind source separation algorithm for noisy mixtures is proposed. We consider ambient background noise signals and nonstationary target source signals. The proposed blind source separation combines signal estimation from noisy observations with source identication through mixing parameter estimation. The sparseness property of target source signals enables the proposed noisy, underdetermined, binaural blind source separation. A minimum mean square error estimator in frequency domain is implemented to estimate the signal spectra from noisy observations. The K-means clustering algorithm is utilized to identify the sources. With the help of calculating the signal absence probability for each frequency bin, noises are effectively eliminated from the target source signals and the mixing parameter estimation becomes more accurate in noisy environments.