# A Closer Look at Weak Label Learning for Audio Events

arXiv: https://arxiv.org/abs/1804.09288 · HTML: https://mlsanity.com/paper/1804.09288/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 12 of 12)

- [PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation](https://mlsanity.com/paper/2102.01243/) (2021)
- [Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition](https://mlsanity.com/paper/2205.03433/) (2022)
- [Weakly-Supervised Audio-Visual Segmentation](https://mlsanity.com/paper/2311.15080/) (2023)
- [CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification](https://mlsanity.com/paper/2203.06760/) (2022)
- [Semantic Grouping Network for Audio Source Separation](https://mlsanity.com/paper/2407.03736/) (2024)
- [The impact of non-target events in synthetic soundscapes for sound event detection](https://mlsanity.com/paper/2109.14061/) (2021)
- [Training image classifiers using Semi-Weak Label Data](https://mlsanity.com/paper/2103.10608/) (2021)
- [Deciphering GunType Hierarchy through Acoustic Analysis of Gunshot Recordings](https://mlsanity.com/paper/2506.20609/) (2025)
- [Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detection](https://mlsanity.com/paper/2103.12388/) (2021)
- [Importance of negative sampling in weak label learning](https://mlsanity.com/paper/2309.13227/) (2023)
- [The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time](https://mlsanity.com/paper/2502.09363/) (2025)
- [Ontological Learning from Weak Labels](https://mlsanity.com/paper/2203.02483/) (2022)

## Abstract (arXiv)

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.