Abstract
Gabor filters have long been proposed as models for spectro-temporal receptive fields (STRFs), with their specific spectral and temporal rate of modulation qualitatively replicating characteristics of STRF filters estimated from responses to auditory stimuli in physiological data. The present study builds on the Gabor-STRF model by proposing a methodology to quantitatively decompose STRFs into a set of optimally matched Gabor filters through matching pursuit, and by quantitatively evaluating spectral and temporal characteristics of STRFs in terms of the derived optimal Gabor-parameters. To summarize a neuron's spectro-temporal characteristics, we introduce a measure for the “diagonality,” i.e., the extent to which an STRF exhibits spectro-temporal transients which cannot be factorized into a product of a spectral and a temporal modulation. With this methodology, it is shown that approximately half of 52 analyzed zebra finch STRFs can each be well approximated by a single Gabor or a linear combination of two Gabor filters. Moreover, the dominant Gabor functions tend to be oriented either in the spectral or in the temporal direction, with truly “diagonal” Gabor functions rarely being necessary for reconstruction of an STRF's main characteristics. As a toy example for the applicability of STRF and Gabor-STRF filters to auditory detection tasks, we use STRF filters as features in an automatic event detection task and compare them to idealized Gabor filters and mel-frequency cepstral coefficients (MFCCs). STRFs classify a set of six everyday sounds with an accuracy similar to reference Gabor features (94% recognition rate). Spectro-temporal STRF and Gabor features outperform reference spectral MFCCs in quiet and in low noise conditions (down to 0 dB signal to noise ratio).
Highlights
Robust detection and identification of behaviorally relevant sounds in possibly adverse acoustic conditions is routinely performed by animals and humans
Since the obtained STRF pattern is the result of a combined stimulation, neuronal processing and statistical estimation procedure, it generally depends on a multitude of factors including animal species, stimulus ensemble, linear, nonlinear, static and time-varying neuronal response characteristics, as well as the statistical inference method employed
In part (B), we use a data set of acoustic event recordings as a toy example for application of STRF-based and Gabor filters as a frontend for sound classification
Summary
Robust detection and identification of behaviorally relevant sounds in possibly adverse acoustic conditions is routinely performed by animals and humans. Limitations to the linear and time-invariant STRF model have been investigated by several authors They may result from higher-order statistics or non-stationarity in stimulus ensembles, non-linear neuronal processing or neuronal plasticity (Sahani and Linden, 2003; Kvale and Schreiner, 2004; Machens et al, 2004; Valentine and Eggermont, 2004; Fritz et al, 2005; Nagel and Doupe, 2006; Christianson et al, 2008) and require specific algorithms for the reliable estimation of underlying STRFs (Sharpee et al, 2004; Meyer et al, 2014a,b, 2015)
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