  Sound event detection is a challenging task, especially for scenes with
multiple simultaneous events. While event classification methods tend to be
fairly accurate, event localization presents additional challenges, especially
when large amounts of labeled data are not available. Task4 of the 2018 DCASE
challenge presents an event detection task that requires accuracy in both
segmentation and recognition of events while providing only weakly labeled
training data. Supervised methods can produce accurate event labels but are
limited in event segmentation when training data lacks event timestamps. On the
other hand, unsupervised methods that model the acoustic properties of the
audio can produce accurate event boundaries but are not guided by the
characteristics of event classes and sound categories. We present a hybrid
approach that combines an acoustic-driven event boundary detection and a
supervised label inference using a deep neural network. This framework
leverages benefits of both unsupervised and supervised methodologies and takes
advantage of large amounts of unlabeled data, making it ideal for large-scale
weakly labeled event detection. Compared to a baseline system, the proposed
approach delivers a 15% absolute improvement in F-score, demonstrating the
benefits of the hybrid bottom-up, top-down approach.
