| Paper: | MLSP-L4.6 | ||
| Session: | Blind Source Separation I | ||
| Time: | Thursday, May 18, 18:10 - 18:30 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Speech and Audio Processing Applications | ||
| Title: | A Biologically-Inspired Approach to the Cocktail Party Problem | ||
| Authors: | Mounya Elhilali, Shihab Shamma, University of Maryland, United States | ||
| Abstract: | Though seemingly effortless, our auditory system engages in complex processes and transformations which enable us to segregate speech and other sounds in cocktail party settings. This paper presents a computational approach to modelling monaural auditory scene analysis, where we attempt to account for perceptual and neuronal findings of receptive field selectivity and adaptation in the auditory cortex. The model introduces a biologically-inspired scheme of dynamic segregation of auditory streams, based on unsupervised clustering and the statistical theory of Kalman prediction. Our method demonstrates its ability to emulate known percepts reported by human subjects in auditory streaming and sound organization tests, and yields successful results in segregating speech from concurrent speaker and music interferences. | ||
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