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2018

A multi-device dataset for urban acoustic scene classification

Mesaros, Annamaria, Heittola, Toni, Virtanen, Tuomas

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This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task.

As in previous years of the challenge, the task is defined for classification of short audio samples into one of predefined acoustic scene classes, using a supervised, closed-set classification setup.
The newly recorded TUT Urban Acoustic Scenes 2018 dataset consists of ten different acoustic scenes and was recorded in six large European cities, therefore it has a higher acoustic variability than the previous datasets used for this task, and in addition to high-quality binaural recordings, it also includes data recorded with mobile devices.
We also present the baseline system consisting of a convolutional neural network and its performance in the subtasks using the recommended cross-validation setup.

A multi-device dataset for urban acoustic scene classification · Around

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Original
2014
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Then

M. Valenti, S. Squartini, A. Diment, G. Parascandolo, and T. Virtanen, “A convolutional neural network approach for acoustic scene classification,” in
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Later among the works it cites.
A. Mesaros, T. Heittola, E. Benetos, P. Foster, M. Lagrange, T. Virtanen, and M. D. Plumbley, “Detection and classification of acoustic scenes and events: Outcome of the dcase 2016 challenge,”
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A. Mesaros, T. Heittola, and T. Virtanen, “Acoustic scene classification: an overview of dcase 2017 challenge entries,” in
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Closest in time.

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