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The paper describes a new approach to sound source separation, called computational auditory scene analysis-enhanced beamforming (CASA-EB). It achieves increased signal separation performance by combining beamforming with an auditory model (CASA). The motivation for combining them is that they are complementary, i.e., they use independent signal attributes, and as a result, CASA-EB can separate more types of mixtures. The efficacy of this approach is demonstrated by experimental results.