A multi-device dataset for urban acoustic scene classification
  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.
