  The development of audio event recognition systems require 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 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 strongly labeled web data by a considerable
margin. Moreover, even though we learn from weakly labeled data, where event
time stamps within the recording are not available during training, our
proposed framework is able to localize events during the inference stage.
