Detection and Classification of Acoustic Scenes and Events

Abstract : For intelligent systems to make best use of the audio modality, it is important that they can recognise not just speech and music, which have been researched as specific tasks, but also general sounds in everyday environments. To stimulate research in this field we conducted a public research challenge: the IEEE Audio and Acoustic Signal Processing Technical Committee challenge on Detection and Classification of Acoustic Scenes and Events (DCASE). In this paper we report on the state of the art in automatically classifying audio scenes, and automatically detecting and classifying audio events. We survey prior work as well as the state of the art represented by the submissions to the challenge from various research groups. We also provide detail on the organisation of the challenge, so that our experience as challenge hosts may be useful to those organising challenges in similar domains. We created new audio datasets and baseline systems for the challenge: these, as well as some submitted systems, are publicly available under open licences, to serve as benchmark for further research in general-purpose machine listening.
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Contributor : Mathieu Lagrange <>
Submitted on : Thursday, March 5, 2015 - 2:01:43 PM
Last modification on : Thursday, January 10, 2019 - 2:56:03 PM
Document(s) archivé(s) le : Saturday, June 6, 2015 - 10:51:09 AM


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  • HAL Id : hal-01123760, version 1


Dan Stowell, Dimitrios Giannoulis, Emmanouil Benetos, Mathieu Lagrange, Mark D. Plumbley. Detection and Classification of Acoustic Scenes and Events. 2015. ⟨hal-01123760⟩



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