# Learning Sound Events From Webly Labeled Data

arXiv: https://arxiv.org/abs/1811.09967 · HTML: https://mlsanity.com/paper/1811.09967/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [ARCA23K: An audio dataset for investigating open-set label noise](https://mlsanity.com/paper/2109.09227/) (2021)
- [Deciphering GunType Hierarchy through Acoustic Analysis of Gunshot Recordings](https://mlsanity.com/paper/2506.20609/) (2025)

## Abstract (arXiv)

In the last couple of years, weakly labeled learning has turned out to be an exciting approach for audio event detection. In this work, we introduce webly labeled learning for sound events which aims to remove human supervision altogether from the learning process. We first develop a method of obtaining labeled audio data from the web (albeit noisy), in which no manual labeling is involved. We then describe methods to efficiently learn from these webly labeled audio recordings. In our proposed system, WeblyNet, two deep neural networks co-teach each other to robustly learn from webly labeled data, leading to around 17% relative improvement over the baseline method. The method also involves transfer learning to obtain efficient representations