Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web
  Data   The development of audio event recognition models requires labeled training
data, which are generally hard to obtain. One promising source of recordings of
audio events is the large amount of multimedia data on the web. In particular,
if the audio content analysis must itself be performed on web audio, it is
important to train the recognizers themselves from such data. Training from
these web data, however, poses several challenges, the most important being the
availability of labels : labels, if any, that may be obtained for the data are
generally {\em weak}, and not of the kind conventionally required for training
detectors or classifiers. We propose that learning algorithms that can exploit
weak labels offer an effective method to learn from web data. We then propose a
robust and efficient deep convolutional neural network (CNN) based framework to
learn audio event recognizers from weakly labeled data. The proposed method can
train from and analyze recordings of variable length in an efficient manner and
outperforms a network trained with {\em strongly labeled} web data by a
considerable margin.

8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph