Auditory textures and primitive auditory scene analysis

Abstract

Event Abstract Back to Event Auditory textures and primitive auditory scene analysis Richard E. Turner1* and Maneesh Sahani2 1 Computational and Biological Learning Lab , United Kingdom 2 UCL, Gatsby Computational Neuroscience Unit, United Kingdom Both the categorical identity of a natural sound and the perceptual analysis of an auditory scene often appear to depend on the statistical properties of the sound and of the acoustic environment. An example of the first case is the sound of "rain"; no two rain sounds are identical because the precise arrangement of falling water droplets is never repeated. Thus, the identity must lie not in the precise waveform but in its statistical properties, such as those relating to the rate of falling rain-drops, the distribution of droplet sizes, and so on. Our perception of other auditory "textures", such as running water, wind or fire, is similar. The statistical properties of a broader class of natural sounds in turn influence how we parse an auditory scene. We have argued previously (Cosyne 2008) that a number of the "Gestalt" phenomena of auditory scene analysis are well captured by the process of inference within an appropriate probabilistic model. Here, we present a new probabilistic generative model for natural sounds, which is both more comprehensive and more numerically tractable than previous such models. The model comprises a set of narrow-band Gaussian carriers that are modulated by a set of positive envelopes. These envelopes are determined by mixing slowly-varying Gaussian processes and passing the result through a positive non-linearity. The parameters of this model – the power, centre-frequencies and bandwidths of the carriers, the time-scale, and depth of the modulation and the patterns of comodulation – were learned from training sounds using approximate maximum-likelihood methods. We show that the model is able to capture many of the important statistics of auditory textures, and can thus be used to synthesise realistic versions of running water, wind, rain and fire. The same generative model was also used to capture many of the basic principles by which listeners appear to understand simple stimuli. In the generative framework, the carriers and the envelopes are latent variables being inferred from the sound waveform. We show that the inferred values of these latent processes correspond to perceptual principles of grouping by proximity, good continuation and common-fate as well as the continuity illusion, comodulation masking release, and the old plus new heuristic (Bregman 1990). Thus, our results suggest that the auditory system is optimised to process sounds with naturalistic statistics, and that a model that captures the statistics of the natural environment can be used to both synthesize perceptually valid "natural" sounds, and to account for a range of perceptual phenomena. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session II Citation: Turner RE and Sahani M (2010). Auditory textures and primitive auditory scene analysis. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00198 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 03 Mar 2010; Published Online: 03 Mar 2010. * Correspondence: Richard E Turner, Computational and Biological Learning Lab, Paris, United Kingdom, turner@gatsby.ucl.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Richard E Turner Maneesh Sahani Google Richard E Turner Maneesh Sahani Google Scholar Richard E Turner Maneesh Sahani PubMed Richard E Turner Maneesh Sahani Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.