Extracting Auditory Cues in Tone-in-Noise detection with a Sparse Feature Selection Algorithm
2011; Frontiers Media; Volume: 5; Linguagem: Inglês
10.3389/conf.fncom.2011.53.00074
ISSN1662-5188
Autores Tópico(s)Advanced Adaptive Filtering Techniques
ResumoEvent Abstract Back to Event Extracting Auditory Cues in Tone-in-Noise detection with a Sparse Feature Selection Algorithm Vinzenz H. Schönfelder1, 2* and Felix A. Wichmann3, 4 1 Technische Universität Berlin, Modelling of Cognitive Processes, Faculty for Electrical Engineering and Computer Science, Germany 2 Bernstein Center for Computational Neuroscience Berlin, Germany 3 Eberhard Karls Universität Tübingen, Neural Information Processing, Wilhelm Schickard Institute of Computer Science, Germany 4 Bernstein Center for Computational Neuroscience Tübingen, Germany Introduction: As a classical paradigm in auditory psychophysics, Tone-in-Noise (TiN) detection still presents a challenge as regards the question which auditory cues human observers use to detect the signal tone (Fletcher, 1938). For narrow band noise, no conclusive answer has been given as to which stimulus features explain observer behavior on a trial-by-trial level (Davidson, 2009). In the present study a large behavioral data set for TiN detection was analyzed with a modern machine learning algorithm, L1-regularized logistic regression (Tibshirani, 1996). Enforcing sparse solutions, this method serves as a feature selection technique allowing the identification of the set of features that is critical to explain observer behavior (Schönfelder and Wichmann, 2011). Methods: An extensive data set (>20'000 trials/observer) was collected with six naïve observers performing TiN detection in a yes/no paradigm. Stimuli were short (200 ms) sound burst consisting of a narrow band gaussian noise masker (100 Hz) centred around a signal tone (500 Hz). Data was collected in blocks with fixed signal-to-noise ratios (SNRs) at four levels along the slope of the psychometric function. Data on response consistency was also collected, estimated from responses to pairs of similar stimuli and serving as a measure of reproducibility of single trial decisions. Subsequently, linear observer models were fit to the data with an L1-regularized logistic regression, for each observer and each SNR separately. The set of features used during data fitting consisted of three components: energy, sound spectrum and envelope spectrum, with each component comprising one (energy) or multiple (spectra) scalar entries characterizing the presented sound. Results: In terms of the psychometric function, observers could hardly be distinguished, only one – a trained musician – had a significantly lower threshold than the rest. Nevertheless, the analysis of perceptual features resulted in two groups of subjects using different combinations of auditory cues, as already observed by Richards (1993). Energy alone, as suggested by Green and Swets (1966), was not sufficient to explain responses, nor was the shape of the envelope spectrum, as proposed by Dau (1996). Instead, most observers relied dominantly on a mixture of sound energy and asymmetric spectral filters, with a peak frequency centered above the signal tone and a negative lobe below. These filters may correspond to off-frequency listening effects or result from the asymmetry of the auditory filters. The results suggest that observers relied on multiple detectors instead of one single feature in this task. Differences in detection strategy across different SNR were not observed. In general, observers showed poor consistency in their responses, in particular for low SNR. Nevertheless, single-trial predictions from the extracted observer models were reliable within the boundaries dictated by response consistency (Neri, 2006). Acknowledgements This research was funded, in part, by the Bernstein Computational Neuroscience Program of the German Federal Ministry of Education and Research (Förderkennzeichen 01GQ0414). References Dau, T., Püschel, D., and Kohlrausch, A. (1996). A quantitative model of the "effective" signal processing in the auditory system. I. Model structure, J Acoust Soc Am 99, 3615–3622. Davidson, S. A., Gilkey, R. H., Colburn, H. S., and Carney, L. H. (2009a). An evaluation of models for diotic and dichotic detection in reproducible noises, J Acoust Soc Am 126, 1906–1925. Fletcher, H. (1938). Loudness, masking and their relation to the hearing process and the problem of noise measurement, J Acoust Soc Am 9, 42, 275–293. Green, D. M. and Swets, J. A. (1966). Signal Detection Theory and Psychophysics. New York: John Wiley & Sons, Inc.. Neri, P. and Levi, D. M. (2006). Receptive versus perceptive fields from the reverse-correlation viewpoint, Vis Res 46, 2465–2474. Richards, V. M. and Nekrich, R. D. (1993). The incorporation of level and level-invariant cues for the detection of a tone added to noise, J Acoust Soc Am 94, 2560–2574. Schönfelder, V. H. and Wichmann, F. A. (2011). A machine learning algorithm for identifying behaviorally-relevant stimulus features from psychophysical data, submitted. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso, J R Statist Soc B. 58, 267–288. Keywords: Auditory Perception, Feature Selection, L1-regularization, machine learning, Psychophysics, sparseness Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Poster Topic: data analysis and machine learning (please use "data analysis and machine learning" as keyword) Citation: Schönfelder VH and Wichmann FA (2011). Extracting Auditory Cues in Tone-in-Noise detection with a Sparse Feature Selection Algorithm. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00074 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: 23 Aug 2011; Published Online: 04 Oct 2011. * Correspondence: Mr. Vinzenz H Schönfelder, Technische Universität Berlin, Modelling of Cognitive Processes, Faculty for Electrical Engineering and Computer Science, Berlin, Germany, vinzenz@e.mail.de 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 Vinzenz H Schönfelder Felix A Wichmann Google Vinzenz H Schönfelder Felix A Wichmann Google Scholar Vinzenz H Schönfelder Felix A Wichmann PubMed Vinzenz H Schönfelder Felix A Wichmann 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.
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