# Audio Event Detection using Weakly Labeled Data

arXiv: https://arxiv.org/abs/1605.02401 · HTML: https://mlsanity.com/paper/1605.02401/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2016.

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

- [SpeechCraft: A Fine-grained Expressive Speech Dataset with Natural Language Description](https://mlsanity.com/paper/2408.13608/) (2024)
- [Weakly-Supervised Audio-Visual Segmentation](https://mlsanity.com/paper/2311.15080/) (2023)
- [Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture](https://mlsanity.com/paper/2105.13854/) (2021)
- [Improving weakly supervised sound event detection with self-supervised auxiliary tasks](https://mlsanity.com/paper/2106.06858/) (2021)
- [A Comparative Study of Speaker Role Identification in Air Traffic Communication Using Deep Learning Approaches](https://mlsanity.com/paper/2111.02041/) (2021)
- [Towards Event Extraction from Speech with Contextual Clues](https://mlsanity.com/paper/2401.15385/) (2024)
- [Deciphering GunType Hierarchy through Acoustic Analysis of Gunshot Recordings](https://mlsanity.com/paper/2506.20609/) (2025)
- [mil-benchmarks: Standardized Evaluation of Deep Multiple-Instance Learning Techniques](https://mlsanity.com/paper/2105.01443/) (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)
- [VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild](https://mlsanity.com/paper/2303.06048/) (2023)
- [Geo-ATBench: A Benchmark for Geospatial Audio Tagging with Geospatial Semantic Context](https://mlsanity.com/paper/2603.10623/) (2026)

## Abstract (arXiv)

Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data. However, the labels available for majority of multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data. We first show that audio event detection using weak labels can be formulated as an Multiple Instance Learning problem. We then suggest two frameworks for solving multiple-instance learning, one based on support vector machines, and the other on neural networks. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Moreover, it can not only detect events in a recording but can also provide temporal locations of events in the recording. This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.