Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Shamma, S.Subject: search.filters.subject.neural systems

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    Ripple Analysis in Ferret Primary Auditory Cortex III. Prediction of Unit Responses to Arbitrary Spectral Profiles
    (1995) Shamma, S.; Versnel, H.; ISR
    We examined whether AI responses to arbitrary spectral profiles can be explained by the superposition of responses to the individual ripple components that make up the spectral pattern. For each unit, the ripple transfer function was first measured using ripple stimuli consisting of broadband complexes with sinusoidally modulated spectral envelopes (Shamma et al. 1994). Unit responses to various combinations of ripples were compared to those predicted from the superposition of responses according to the transfer function. Spectral profiled included combinations of 2-5 ripples of equal amplitudes and random phases, and vowel- like profiles composed of 10 ripples with various amplitudes and phases. The results demonstrate that predicted and measured responses are reasonably well matched, and hence support the notion that AI analyzes the acoustic spectrum in a substantially linear manner.
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    Normalization and Noise-Robustness in Early Auditory Representations
    (1993) Wang, K.; Shamma, S.; ISR
    A common sequence of operations in the early stages of most sensory systems is a multiscale transform followed by a compressive nonlinearity. In this paper, we explore the contribution of these operations to the formation of robust and perceptually significant representation in the early auditory system. It is shown that auditory representation of the acoustic spectrum is effectively a self-normalized spectral analysis, i.e., the auditory system computes a spectrum that is divided by a smoothed version of itself. Such a self-normalization induces significant effects such as spectral shape enhancement and robustness against scaling and noise corruption. Examples using synthesized signals and a natural speech vowel are presented to illustrate these results. Furthermore, the characteristics of auditory representation are discussed in the context of several psychoacoustical findings, together with the possible benefits of this model for various engineering applications.
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    Classification of the Transient Signals via Auditory Representations
    (1991) Teolis, A.; Shamma, S.; ISR
    We use a model of processing in the human auditory system to develop robust representations of signals. These reduced representations are then presented to a neural network for training and classification.

    Empirical studies demonstrate that auditory representations compare favorably to direct frequency (magnitude spectrum) representations with respect to classification performance (i.e. probabilities of detection and false alarm). For this comparison the Receiver Operating Characteristic (ROC) curves are generated from signals derived from the standard transient data set (STDS) distributed by DARPA/ONR.