Title: Weakly supervised CRNN system for sound event detection with large-scale unlabeled in-domain data,
Abstract: Sound event detection (SED) is typically posed as a supervised learning
problem requiring training data with strong temporal labels of sound events.
However, the production of datasets with strong labels normally requires
unaffordable labor cost. It limits the practical application of supervised SED
methods. The recent advances in SED approaches focuses on detecting sound
events by taking advantages of weakly labeled or unlabeled training data. In
this paper, we propose a joint framework to solve the SED task using
large-scale unlabeled in-domain data. In particular, a state-of-the-art general
audio tagging model is first employed to predict weak labels for unlabeled
data. On the other hand, a weakly supervised architecture based on the
convolutional recurrent neural network (CRNN) is developed to solve the strong
annotations of sound events with the aid of the unlabeled data with predicted
labels. It is found that the SED performance generally increases as more
unlabeled data is added into the training. To address the noisy label problem
of unlabeled data, an ensemble strategy is applied to increase the system
robustness. The proposed system is evaluated on the SED dataset of DCASE 2018
challenge. It reaches a F1-score of 21.0%, resulting in an improvement of 10%
over the baseline system.