  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.
