A computational model of auditory analysis is described that is inspired by psychoacoustical and neurophysiological findings in early and central stages of the auditory system. The model provides a unified multiresolution representation of the spectral and temporal features likely critical in the perception of sound. Simplified, more specifically tailored versions of this model have already been validated by successful application in the assessment of speech intelligibility [Elhilali et al., Speech Commun. 41(2-3), 331-348 (2003); Chi et al., J. Acoust. Soc. Am. 106, 2719-2732 (1999)] and in explaining the perception of monaural phase sensitivity [R. Carlyon and S. Shamma, J. Acoust. Soc. Am. 114, 333-348 (2003)]. Here we provide a more complete mathematical formulation of the model, illustrating how complex signals are transformed through various stages of the model, and relating it to comparable existing models of auditory processing. Furthermore, we outline several reconstruction algorithms to resynthesize the sound from the model output so as to evaluate the fidelity of the representation and contribution of different features and cues to the sound percept.

Detection thresholds for spectral and temporal modulations are measured using broadband spectra with sinusoidally rippled profiles that drift up or down the log-frequency axis at constant velocities. Spectro-temporal modulation transfer functions (MTFs) are derived as a function of ripple peak density (Ω cycles/octave) and drifting velocity (ω Hz). The MTFs exhibit a low-pass function with respect to both dimensions, with 50% bandwidths of about 16 Hz and 2 cycles/octave. The data replicate (as special cases) previously measured purely temporal MTFs (Ω=0) [Viemeister, J. Acoust. Soc. Am. 66, 1364–1380 (1979)] and purely spectral MTFs (ω=0) [Green, in Auditory Frequency Selectivity (Plenum, Cambridge, 1986), pp. 351–359]. A computational auditory model is presented that exhibits spectro-temporal MTFs consistent with the salient trends in the data. The model is used to demonstrate the potential relevance of these MTFs to the assessment of speech intelligibility in noise and reverberant conditions.

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To form a reliable, consistent, and accurate representation of the acoustic scene, a reasonable conjecture is that cortical neurons maintain stable receptive fields after an early period of developmental plasticity. However, recent studies suggest that cortical neurons can be modified throughout adulthood and may change their response properties quite rapidly to reflect changing behavioral salience of certain sensory features. Because claims of adaptive receptive field plasticity could be confounded by intrinsic, labile properties of receptive fields themselves, we sought to gauge spontaneous changes in the responses of auditory cortical neurons. In the present study, we examined changes in a series of spectrotemporal receptive fields (STRFs) gathered from single neurons in successive recordings obtained over time scales of 30 -120 min in primary auditory cortex (A1) in the quiescent, awake ferret. We used a global analysis of STRF shape based on a large database of A1 receptive fields. By clustering this STRF space in a data-driven manner, STRF sequences could be classified as stable or labile. We found that Ͼ73% of A1 neurons exhibited stable receptive field attributes over these time scales. In addition, we found that the extent of intrinsic variation in STRFs during the quiescent state was insignificant compared with behaviorally induced STRF changes observed during performance of spectral auditory tasks. Our results confirm that task-related changes induced by attentional focus on specific acoustic features were indeed confined to behaviorally salient acoustic cues and could be convincingly attributed to learning-induced plasticity when compared with "spontaneous" receptive field variability.