  The design of new methods and models when only weakly-labeled data are
available is of paramount importance in order to reduce the costs of manual
annotation and the considerable human effort associated with it. In this work,
we address Sound Event Detection in the case where a weakly annotated dataset
is available for training. The weak annotations provide tags of audio events
but do not provide temporal boundaries. The objective is twofold: 1) audio
tagging, i.e. multi-label classification at recording level, 2) sound event
detection, i.e. localization of the event boundaries within the recordings.
This work focuses mainly on the second objective. We explore an approach
inspired by Multiple Instance Learning, in which we train a convolutional
recurrent neural network to give predictions at frame-level, using a custom
loss function based on the weak labels and the statistics of the frame-based
predictions. Since some sound classes cannot be distinguished with this
approach, we improve the method by penalizing similarity between the
predictions of the positive classes during training. On the test set used in
the DCASE 2018 challenge, consisting of 288 recordings and 10 sound classes,
the addition of a penalty resulted in a localization F-score of 34.75%, and
brought 10% relative improvement compared to not using the penalty. Our best
model achieved a 26.20% F-score on the DCASE-2018 official Eval subset close to
the 10-system ensemble approach that ranked second in the challenge with a
29.9% F-score.
