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@conference{b8455a768acb4fce9d8bb47592a6e7de, author = "Annamaria Mesaros and Toni Heittola and Tuomas Virtanen", abstract = "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.", booktitle = "Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop", keywords = "Acoustic scene classification", pages = "9--13", title = "{A} multi-device dataset for urban acoustic scene classification", year = "2018", }