  Audio content analysis in terms of sound events is an important research
problem for a variety of applications. Recently, the development of weak
labeling approaches for audio or sound event detection (AED) and availability
of large scale weakly labeled dataset have finally opened up the possibility of
large scale AED. However, a deeper understanding of how weak labels affect the
learning for sound events is still missing from literature. In this work, we
first describe a CNN based approach for weakly supervised training of audio
events. The approach follows some basic design principle desirable in a
learning method relying on weakly labeled audio. We then describe important
characteristics, which naturally arise in weakly supervised learning of sound
events. We show how these aspects of weak labels affect the generalization of
models. More specifically, we study how characteristics such as label density
and corruption of labels affects weakly supervised training for audio events.
We also study the feasibility of directly obtaining weak labeled data from the
web without any manual label and compare it with a dataset which has been
manually labeled. The analysis and understanding of these factors should be
taken into picture in the development of future weak label learning methods.
Audioset, a large scale weakly labeled dataset for sound events is used in our
experiments.

Sound;Machine Learning;Multimedia;Audio and Speech Processing