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A Tutorial on Auditory Attention Identification Methods
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Oticon AS, Denmark.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Linköping University, The Swedish Institute for Disability Research. Oticon AS, Denmark; Tech Univ Denmark, Denmark.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2019 (English)In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 13, article id 153Article in journal (Refereed) Published
Abstract [en]

Auditory attention identification methods attempt to identify the sound source of a listeners interest by analyzing measurements of electrophysiological data. We present a tutorial on the numerous techniques that have been developed in recent decades, and we present an overview of current trends in multivariate correlation-based and model-based learning frameworks. The focus is on the use of linear relations between electrophysiological and audio data. The way in which these relations are computed differs. For example, canonical correlation analysis (CCA) finds a linear subset of electrophysiological data that best correlates to audio data and a similar subset of audio data that best correlates to electrophysiological data. Model-based (encoding and decoding) approaches focus on either of these two sets. We investigate the similarities and differences between these linear model philosophies. We focus on (1) correlation-based approaches (CCA), (2) encoding/decoding models based on dense estimation, and (3) (adaptive) encoding/decoding models based on sparse estimation. The specific focus is on sparsity-driven adaptive encoding models and comparing the methodology in state-of-the-art models found in the auditory literature. Furthermore, we outline the main signal processing pipeline for how to identify the attended sound source in a cocktail party environment from the raw electrophysiological data with all the necessary steps, complemented with the necessary MATLAB code and the relevant references for each step. Our main aim is to compare the methodology of the available methods, and provide numerical illustrations to some of them to get a feeling for their potential. A thorough performance comparison is outside the scope of this tutorial.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA , 2019. Vol. 13, article id 153
Keywords [en]
cocktail-party problem; auditory attention; linear models; stimulus reconstruction; canonical correlation anaysis (CCA); decoding; encoding; sparse representation
National Category
Psychology (excluding Applied Psychology)
Identifiers
URN: urn:nbn:se:liu:diva-155935DOI: 10.3389/fnins.2019.00153ISI: 000461627500001OAI: oai:DiVA.org:liu-155935DiVA, id: diva2:1301300
Note

Funding Agencies|Oticon Foundation; EU H2020-ICT COCOHA (Cognitive Control of a Hearing Aid) grant [644732]; Swedish Research Council (Vetenskapsradet) [VR 2017-06092]

Available from: 2019-04-01 Created: 2019-04-01 Last updated: 2019-10-15

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Alickovic, EminaLunner, ThomasGustafsson, FredrikLjung, Lennart
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