PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Modeling Natural Sounds With Modulation Cascade Processes
Richard Turner and Maneesh Sahani
Advances in Neural Information Processing Systems Volume 21, 2007.

Abstract

Natural sounds are structured on many time-scales. A typical segment of speech, for example, contains features that span four orders of magnitude: Sentences ( ~1s); phonemes ( ~0.1s); glottal pulses ( ~0.01 s); and formants (~0.001 s). The auditory system uses information from each of these time-scales to solve complicated tasks such as auditory scene analysis. One route toward understanding how auditory processing accomplishes this analysis is to build neuroscienceinspired algorithms which solve similar tasks and to compare the properties of these algorithms with properties of auditory processing. There is however a discord: Current machine-audition algorithms largely concentrate on the shorter time-scale structures in sounds, and the longer structures are ignored. The reason for this is two-fold. Firstly, it is a difficult technical problem to construct an algorithm that utilises both sorts of information. Secondly, it is computationally demanding to simultaneously process data both at high resolution (to extract short temporal information) and for long duration (to extract long temporal information). The contribution of this work is to develop a new statistical model for natural sounds that captures structure across a wide range of time-scales, and to provide efficient learning and inference algorithms. We demonstrate the success of this approach on a missing data task.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Speech
Theory & Algorithms
ID Code:3788
Deposited By:Richard Turner
Deposited On:25 February 2008