Multiple Neural Networks with Ensemble Method for Audio Tagging with Noisy Labels and Minimal Supervision

Abstract

In this paper, we describe our system for the Task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 Challenge: Audio tagging with noisy labels and minimal supervision. This task provides a small amount of verified data (curated data) and a larger quantity of unverified data (noisy data) as training data. Each audio clip contains one or more sound events, so it can be considered as a multi-label audio classification task. To tackle this problem, we mainly use four strategies. The first is a sigmoid-softmax activation to deal with so-called sparse multi-label classification. The second is a staged training strategy to learn from noisy data. The third is a post-processing method that normalizes output scores for each sound class. The last is an ensemble method that averages models learned with multiple neural networks and various acoustic features. All of the above strategies contribute to our system significantly. Our final system achieved labelweighted label-ranking average precision (lwlrap) scores of 0.758 on the private test dataset and 0.742 on the public test dataset, winning the 2nd place in DCASE 2019 Challenge Task 2.