Making sense of periodicity glimpses in a prediction-update-loop - a computational model of attentive voice tracking

Joanna Luberadzka, Hendrik Kayser & Volker Hohmann
Humans are able to follow a given speaker even in challenging acoustic conditions. The perceptual mechanisms underlying this ability remain unclear. In this study, we present a computational model of attentive voice tracking, consisting of four main computational blocks: A) sparse periodicity-based auditory feature extraction, B) foreground-background segregation, C) state estimation and D) top-down knowledge. Conceptually, the model brings together ideas related to auditory glimpses, foreground-background segregation and Bayesian inference. Algorithmically, it combines sparse periodicity...
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