Multi-Scale Convolutional Recurrent Neural Network with Ensemble Method for Weakly Labeled Sound Event Detection

In this paper, we describe our contributions to the challenge of detection and classification of acoustic scenes and events. We propose multi-scale convolutional recurrent neural network(Multi-scale CRNN), a novel weakly-supervised learning framework for sound event detection. By integrating information from different time resolutions, the multi-scale method can capture both the fine-grained and coarse-grained features of sound events and model the temporal dependency including fine-grained dependency and long-term dependency. Furthermore, the ensemble method proposed in the paper reduces the frame-level prediction errors using classification results. The proposed method achieves 29.2% in the event-based F1-score and 1.40 in event-based error rate in development set of DCASE2018 task4 compared to the baseline of 14.1% F-value and 1.54 error rate [1].