A Closer Look at Weak Label Learning for Audio Events

TL;DR

This paper investigates how weak labels influence audio event detection, analyzing factors like label density and corruption, and compares web-derived labels with manually annotated data to enhance understanding of weak label learning.

Contribution

It provides a detailed analysis of weak label characteristics affecting model generalization and introduces a CNN-based approach for weakly supervised audio event training.

Findings

Label density impacts model performance significantly.

Corrupted labels reduce detection accuracy.

Web-derived weak labels are comparable to manual labels in some cases.

Abstract

Audio content analysis in terms of sound events is an important research problem for a variety of applications. Recently, the development of weak labeling approaches for audio or sound event detection (AED) and availability of large scale weakly labeled dataset have finally opened up the possibility of large scale AED. However, a deeper understanding of how weak labels affect the learning for sound events is still missing from literature. In this work, we first describe a CNN based approach for weakly supervised training of audio events. The approach follows some basic design principle desirable in a learning method relying on weakly labeled audio. We then describe important characteristics, which naturally arise in weakly supervised learning of sound events. We show how these aspects of weak labels affect the generalization of models. More specifically, we study how characteristics such as label density and corruption of labels affects weakly supervised training for audio events. We also study the feasibility of directly obtaining weak labeled data from the web without any manual label and compare it with a dataset which has been manually labeled. The analysis and understanding of these factors should be taken into picture in the development of future weak label learning methods. Audioset, a large scale weakly labeled dataset for sound events is used in our experiments.