Guided multi-branch learning systems for sound event detection with sound separation

TL;DR

This paper introduces a multi-branch learning approach with sound separation to enhance sound event detection systems, demonstrating improved performance through diverse pooling strategies and synthetic data utilization.

Contribution

The paper presents a novel multi-branch learning framework incorporating various pooling strategies and sound separation, significantly improving sound event detection accuracy.

Findings

Multi-branch learning improves detection performance.

Sound separation enhances ensemble system accuracy.

Incorporating synthetic data benefits model training.

Abstract

In this paper, we describe in detail our systems for DCASE 2020 Task 4. The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework with an attention-based embedding-level pooling module and a semi-supervised learning approach named guided learning. This year, we incorporate multi-branch learning (MBL) into the original system to further improve its performance. MBL uses different branches with different pooling strategies (including instance-level and embedding-level strategies) and different pooling modules (including attention pooling, global max pooling or global average pooling modules), which share the same feature encoder of the model. Therefore, multiple branches pursuing different purposes and focusing on different characteristics of the data can help the feature encoder model the feature space better and avoid over-fitting. To better exploit the strongly-labeled synthetic data, inspired by multi-task learning, we also employ a sound event detection branch. To combine sound separation (SS) with sound event detection (SED), we fuse the results of SED systems with SS-SED systems which are trained using separated sound output by an SS system. The experimental results prove that MBL can improve the model performance and using SS has great potential to improve the performance of SED ensemble system.