  Audio tagging aims to infer descriptive labels from audio clips. Audio
tagging is challenging due to the limited size of data and noisy labels. In
this paper, we describe our solution for the DCASE 2018 Task 2 general audio
tagging challenge. The contributions of our solution include: We investigated a
variety of convolutional neural network architectures to solve the audio
tagging task. Statistical features are applied to capture statistical patterns
of audio features to improve the classification performance. Ensemble learning
is applied to ensemble the outputs from the deep classifiers to utilize
complementary information. a sample re-weight strategy is employed for ensemble
training to address the noisy label problem. Our system achieves a mean average
precision (mAP@3) of 0.958, outperforming the baseline system of 0.704. Our
system ranked the 1st and 4th out of 558 submissions in the public and private
leaderboard of DCASE 2018 Task 2 challenge. Our codes are available at
https://github.com/Cocoxili/DCASE2018Task2/.
