CRCNS: Auditory Stream Segregation and the Cocktail Party Problem

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DESCRIPTION (provided by applicant): Hearing engages, in a seemingly effortless way, complex processes and transformations collectively known as auditory scene analysis, through which the auditory system consolidates acoustic information from the environment into perceptual and cognitive experiences. The project proposed here explores a fundamental perceptual component of auditory scene analysis called auditory stream segregation. This phenomenon manifests itself in the ability of humans and animals to attend to one of many competing acoustic streams even in extreme noisy and reverberant environments - also known in the literature as the "Cocktail Party Problem". While completely intuitive and omnipresent in humans, mammals, birds, and fish, this remarkable perceptual ability remains shrouded in mystery. It has been rarely quantified in objective psychoacoustical tests or investigated in non-human species, and seldom explored in physiological experiments in humans or animals. Consequently, the few attempts at developing computational models of auditory stream segregation remain highly speculative, and lack the perceptual and physiological data to support their formulations. This in turn has considerably hindered the development of such capabilities in engineering systems such as in automatic speech recognition or the detection and tracking of target sounds in sensor networks. The proposed research seeks to develop a computational model of auditory scene analysis that accounts for perceptual and neuronal-findings of auditory stream segregation. The intellectual merit of this work is providing a rigorous framework for the design of new psychoacoustic and physiological experiments of streaming, and for developing effective algorithmic implementations to tackle the "cocktail party problem" in engineering applications. The proposed research project draws upon the expertise of neurobiologists, psychoacousticians, and engineers in integrating psychoacoustic, physiological and computational techniques. The broader impact of this effort is in providing versatile and tractable models of auditory stream segregation, and hence significantly facilitating the integration of such capabilities in engineering systems, such as in automatic speech recognition or the detection and tracking of target sounds in sensor networks. This project will also provide a rigorous foundation for the design and generation of new hypotheses in order to better understand the neural basis of active listening