Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-task learning for audio tagging, sound event detection spatial
  localization: DCASE 2019 baseline systems   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 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 is a good model for a majority of the
DCASE 2019 tasks.

12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys