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The Detection and Classification of Acoustic Scenes and Events (DCASE)consists of five audio classification and sound event detection tasks: 1)Acoustic scene classification, 2) General-purpose audio tagging of Freesound,3) Bird audio detection, 4) Weakly-labeled semi-supervised sound eventdetection and 5) Multi-channel audio classification. In this paper, we create across-task baseline system for all five tasks based on a convlutional neuralnetwork (CNN): a "CNN Baseline" system. We implemented CNNs with 4 layers and 8layers originating from AlexNet and VGG from computer vision. We investigatedhow the performance varies from task to task with the same configuration ofneural networks. Experiments show that deeper CNN with 8 layers performs betterthan CNN with 4 layers on all tasks except Task 1. Using CNN with 8 layers, weachieve an accuracy of 0.680 on Task 1, an accuracy of 0.895 and a mean averageprecision (MAP) of 0.928 on Task 2, an accuracy of 0.751 and an area under thecurve (AUC) of 0.854 on Task 3, a sound event detection F1 score of 20.8% onTask 4, and an F1 score of 87.75% on Task 5. We released the Python source codeof the baseline systems under the MIT license for further research.

DCASE 2018 Challenge Surrey Cross-Task convolutional neural network baseline