THUEE SYSTEM FOR DCASE 2019 CHALLENGE TASK 2 Technical Report

In this report, we described our submission for the task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 Challenge: Audio tagging with noisy labels and minimal supervision. Our methods are mainly based on two types of deep learning models: Convolutional Recurrent Neural Network (CRNN) and DenseNet. In order to prevent overfitting, we adopted data augmentation using mixup strategy and SpecAugment. Besides, we designed a staged loss function to train our models using both curated and noisy data. We also used various acoustic features, including log-mel energies and perceptual Constant-Q transform (p-CQT), and tried an ensemble of multiple subsystems to enhance the generalization capability of our system. Our final system achieved a lwlrap score of 0.742 on the public leaderboard in Kaggle.