  Audio Event Detection is an important task for content analysis of multimedia
data. Most of the current works on detection of audio events is driven through
supervised learning approaches. We propose a weakly supervised learning
framework which can make use of the tremendous amount of web multimedia data
with significantly reduced annotation effort and expense. Specifically, we use
several multiple instance learning algorithms to show that audio event
detection through weak labels is feasible. We also propose a novel scalable
multiple instance learning algorithm and show that its competitive with other
multiple instance learning algorithms for audio event detection tasks.
