Bayesian Pitch

Abstract

Event Abstract Back to Event Bayesian Pitch Phillipp Hehrmann1* and Maneesh Sahani1 1 UCL, Gatsby Computational Neuroscience Unit, United Kingdom Pitch is a fundamental perceptual attribute of many sounds. It carries melodic, prosodic and sometimes semantic information, and contributes to auditory scene analysis. However, over a century of study has failed to yield a generally-accepted definition of pitch and model of its perception. No single physical feature of the acoustic waveform correlates perfectly with the reported pitch for all tested sounds, raising the question of whether pitch perception instead relies on a combination of several different features, and in turn, mechanisms. Three implicated features are harmonic regularities in the spectrum, periodicity of the waveform envelope, and the timing of fine structure peaks within the envelope. Here, we propose a computationally unified account of pitch perception. Our hypothesis is that the pitch of a sound is a Bayesian estimate of its periodicity, as inferred from the resulting time-varying auditory nerve firing rates. Inference proceeds within a generative model in which pitched sounds are first formed by convolving a regular train of impulses with a randomly drawn impulse-response and adding Gaussian noise. These sounds are then transformed to auditory nerve activity by a cascade of bandpass filters and demodulators, corrupted by further noise. This generative process describes responses to many naturally occurring pitched sounds such as voiced speech, vocalisations and musical instrument sounds. We suggest that the aberrant or ambiguous pitch percepts reported for some laboratory constructed sounds arise because the auditory system infers periodicity assuming this same generative model, even when it does not apply. To test our hypothesis, we chose three types of stimuli to exemplify the three physical cues listed above. Evidence for the importance of spectral features comes from harmonic complex sounds with missing fundamental f0. The pitch of these sounds corresponds to f0, but its strength varies depending on the number of the lowest harmonic present in the spectrum. Evidence for temporal envelope periodicity comes from amplitude modulated noise, which is weakly pitched despite the complete lack of spectral features. Finally, the timing of fine structure peaks must be invoked to explain the ambiguous pitch of amplitude modulated pure tones, which corresponds neither exactly to the envelope modulation rate nor to the spacing of spectral components. In all three cases, our Bayesian periodicity estimate provides a good match with human pitch perception, demonstrating that the model can indeed utilise spectral, temporal envelope and temporal fine structure cues within a single computational framework. Notably, the effect of model mismatch in case of the amplitude modulated tone and noise stimuli are also consistent with human pitch perception, lending further support to our hypothesis. Viewing pitch perception as probabilistic inference relates pitch to an unobserved, yet real physical quantity and provides a normative interpretation of its role in auditory perception, rather than a purely phenomenological match. Whilst this interpretation does not address the issue of neural implementation, it suggests that different neural mechanisms, should they exist, are set up together in a way to achieve a single computational goal. 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: Hehrmann P and Sahani M (2010). Bayesian Pitch. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00199 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: Phillipp Hehrmann, UCL, Gatsby Computational Neuroscience Unit, London, United Kingdom, hehrmann@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 Phillipp Hehrmann Maneesh Sahani Google Phillipp Hehrmann Maneesh Sahani Google Scholar Phillipp Hehrmann Maneesh Sahani PubMed Phillipp Hehrmann 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.