  The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019
challenge focuses on audio tagging, sound event detection and spatial
localisation. DCASE 2019 consists of five tasks: 1) acoustic scene
classification, 2) audio tagging with noisy labels and minimal supervision, 3)
sound event localisation and detection, 4) sound event detection in domestic
environments, and 5) urban sound tagging. In this paper, we propose generic
cross-task baseline systems based on convolutional neural networks (CNNs). The
motivation is to investigate the performance of a variety of models across
several audio recognition tasks without exploiting the specific characteristics
of the tasks. We looked at CNNs with 5, 9, and 13 layers, and found that the
optimal architecture is task-dependent. For the systems we considered, we found
that the 9-layer CNN with average pooling after convolutional layers is a good
model for a majority of the DCASE 2019 tasks.
