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Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge

Mesaros, Annamaria, Heittola, Toni, Benetos, Emmanouil, Foster, Peter, Lagrange, Mathieu, Virtanen, Tuomas and Plumbley, Mark D. (2017) Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge IEEE/ACM Transactions on Audio, Speech and Language Processing, 26 (2). pp. 379-393.

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Abstract

Public evaluation campaigns and datasets promote active development in target research areas, allowing direct comparison of algorithms. The second edition of the challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016) has offered such an opportunity for development of state-of-the-art methods, and succeeded in drawing together a large number of participants from academic and industrial backgrounds. In this paper, we report on the tasks and outcomes of the DCASE 2016 challenge. The challenge comprised four tasks: acoustic scene classification, sound event detection in synthetic audio, sound event detection in real-life audio, and domestic audio tagging. We present in detail each task and analyse the submitted systems in terms of design and performance. We observe the emergence of deep learning as the most popular classification method, replacing the traditional approaches based on Gaussian mixture models and support vector machines. By contrast, feature representations have not changed substantially throughout the years, as mel frequency-based representations predominate in all tasks. The datasets created for and used in DCASE 2016 are publicly available and are a valuable resource for further research.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Mesaros, Annamaria
Heittola, Toni
Benetos, Emmanouil
Foster, Peter
Lagrange, Mathieu
Virtanen, Tuomas
Plumbley, Mark D.m.plumbley@surrey.ac.uk
Date : 29 November 2017
Funders : Engineering and Physical Sciences Research Council (EPSRC)
Identification Number : 10.1109/TASLP.2017.2778423
Grant Title : Making Sense of Sounds
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Uncontrolled Keywords : Acoustic scene classification; Audio datasets; Pattern recognition; Sound event detection;
Depositing User : Clive Harris
Date Deposited : 05 Dec 2017 16:23
Last Modified : 14 Mar 2018 15:34
URI: http://epubs.surrey.ac.uk/id/eprint/845118

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