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One of the unique characteristics of human hearing is its ability to recognize acoustic objects even in presence of severe noise and distortions. In this work, we explore two mechanisms underlying this ability 1) redundant mapping of acoustic waveforms along distributed latent representations and 2) adaptive feedback based on prior knowledge to selectively attend to targets of interest. We propose a bio-mimetic account of acoustic object classification by developing a novel distributed deep belief network validated for the task of robus
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