Detection of speech tokens in noise using adaptive spectrotemporal receptive fields

Neurophysiological studies of sound encoding at the level of auditory cortex paint a picture of an intricate filterbank that encodes detailed spectral and temporal modulations in the sensory input. Furthermore, these filters exhibit adaptive qualities called neural plasticity that shape their tuning parameters in line with behavioral goals of interest. In this work, we explore qualitative principles about how this neuronal reshaping can aid in an enhanced representation of target sounds. Here, we employ a set of parameterized two-dimensional Gabor filters as basis functions that tile the space of neurophysiological spectrotemporal modulations. We examine mechanisms for judiciously retuning parameters of the Gabor filterbank in order to enhance the representation of target sounds of interest. We test the efficacy of this scheme in enhancing representation of sound tokens in adverse noisy backgrounds.