In this technique report, we present a bunch of methods for the task 4 of
Detection and Classification of Acoustic Scenes and Events 2017 (DCASE2017)
challenge. This task evaluates systems for the large-scale detection of sound
events using weakly labeled training data. The data are YouTube video excerpts
focusing on transportation and warnings due to their industry applications.
There are two tasks, audio tagging and sound event detection from weakly
labeled data. Convolutional neural network (CNN) and gated recurrent unit (GRU)
based recurrent neural network (RNN) are adopted as our basic framework. We
proposed a learnable gating activation function for selecting informative local
features. Attention-based scheme is used for localizing the specific events in
a weakly-supervised mode. A new batch-level balancing strategy is also proposed
to tackle the data unbalancing problem. Fusion of posteriors from different
systems are found effective to improve the performance. In a summary, we get
61% F-value for the audio tagging subtask and 0.73 error rate (ER) for the
sound event detection subtask on the development set. While the official
multilayer perceptron (MLP) based baseline just obtained 13.1% F-value for the
audio tagging and 1.02 for the sound event detection.Comment: DCASE2017 challenge ranked 1st system, task4, tech repor
