While many algorithms exist for accurate extraction of formant frequencies from a speech waveform, these algorithms are not typically shown to be robust in the presence of highly-transient background noise such as competing speech waveforms. Preliminary results are presented from an algorithm using time-varying adaptive filters that appears to be robust in the presence of white, Gaussian noise or a single competing speaker over a large range of signal-to-noise ratios (quiet to −6 dB). Use of a synthesized sentence, for which the actual formant frequencies are known, permits quantitative assessment of the algorithm's accuracy as a function of signal-to-noise ratio.

The way groups of auditory neurons interact to code acoustic in(cid:173) formation is investigated using an information theoretic approach. We develop measures of redundancy among groups of neurons, and apply them to the study of collaborative coding efficiency in two processing stations in the auditory pathway: the inferior colliculus (IC) and the primary auditory cortex (AI). Under two schemes for the coding of the acoustic content, acoustic segments coding and stimulus identity coding, we show differences both in information content and group redundancies between IC and AI neurons. These results provide for the first time a direct evidence for redundancy reduction along the ascending auditory pathway, as has been hy(cid:173) pothesized for theoretical considerations [Barlow 1959,2001]. The redundancy effects under the single-spikes coding scheme are signif(cid:173) icant only for groups larger than ten cells, and cannot be revealed with the redundancy measures that use only pairs of cells. The results suggest that the auditory system transforms low level rep(cid:173) resentations that contain redundancies due to the statistical struc(cid:173) ture of natural stimuli, into a representation in which cortical neu(cid:173) rons extract rare and independent component of complex acoustic signals, that are useful for auditory scene analysis.

An application of neural network modeling is described for generating hypotheses about the relationships between response properties of neurons and information processing in the auditory system. The goal is to study response properties that are useful for extracting sound localization information from directionally selective spectral filtering provided by the pinna. For studying sound localization based on spectral cues provided by the pinna, a feedforward neural network model with a guaranteed level of fault tolerance is introduced. Fault tolerance and uniform fault tolerance in a neural network are formally defined and a method is described to ensure that the estimated network exhibits fault tolerance. The problem of estimating weights for such a network is formulated as a large-scale nonlinear optimization problem. Numerical experiments indicate that solutions with uniform fault tolerance exist for the pattern recognition problem considered. Solutions derived by introducing fault tolerance constraints have better generalization properties than solutions obtained via unconstrained back-propagation.

A description is given of an application of neural network modeling to generate hypotheses about how response properties of neurons relate to information processing in the auditory system. Specifically, the kinds of response properties that are useful in extracting sound-localization information from directionally selective pinna filtering provided by the pinna are studied. For studying the sound localization based on spectral cues provided by the pinna, a neural network model with a guaranteed level of fault-tolerance is introduced. The notions of fault-tolerance in neural networks are formally defined, and a method of ensuring that the estimated network exhibits fault tolerance is described. The problem of estimating such weights is formulated as a large-scale constrained nonlinear programming problem. The preliminary numerical experiments indicate that (a) solutions with uniform fault tolerance in the hidden layer exist for this pattern recognition problem and that (b) using fault-tolerance as a constraint leads to solutions that have better generalization than solutions obtained via unconstrained backpropagation algorithm