  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 event
detection and 5) Multi-channel audio classification. In this paper, we create a
cross-task baseline system for all five tasks based on a convlutional neural
network (CNN): a "CNN Baseline" system. We implemented CNNs with 4 layers and 8
layers originating from AlexNet and VGG from computer vision. We investigated
how the performance varies from task to task with the same configuration of
neural networks. Experiments show that deeper CNN with 8 layers performs better
than CNN with 4 layers on all tasks except Task 1. Using CNN with 8 layers, we
achieve an accuracy of 0.680 on Task 1, an accuracy of 0.895 and a mean average
precision (MAP) of 0.928 on Task 2, an accuracy of 0.751 and an area under the
curve (AUC) of 0.854 on Task 3, a sound event detection F1 score of 20.8% on
Task 4, and an F1 score of 87.75% on Task 5. We released the Python source code
of the baseline systems under the MIT license for further research.
