Title: Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network,
Abstract: This paper proposes a neural network architecture and training scheme to
learn the start and end time of sound events (strong labels) in an audio
recording given just the list of sound events existing in the audio without
time information (weak labels). We achieve this by using a stacked
convolutional and recurrent neural network with two prediction layers in
sequence one for the strong followed by the weak label. The network is trained
using frame-wise log mel-band energy as the input audio feature, and weak
labels provided in the dataset as labels for the weak label prediction layer.
Strong labels are generated by replicating the weak labels as many number of
times as the frames in the input audio feature, and used for strong label layer
during training. We propose to control what the network learns from the weak
and strong labels by different weighting for the loss computed in the two
prediction layers. The proposed method is evaluated on a publicly available
dataset of 155 hours with 17 sound event classes. The method achieves the best
error rate of 0.84 for strong labels and F-score of 43.3% for weak labels on
the unseen test split.