# Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments

arXiv: https://arxiv.org/abs/1807.10501 · HTML: https://mlsanity.com/paper/1807.10501/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

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

- [Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection](https://mlsanity.com/paper/2110.02011/) (2021)
- [Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild](https://mlsanity.com/paper/2208.07522/) (2022)
- [Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2402.02694/) (2024)
- [Voice activity detection in the wild: A data-driven approach using teacher-student training](https://mlsanity.com/paper/2105.04065/) (2021)
- [The impact of non-target events in synthetic soundscapes for sound event detection](https://mlsanity.com/paper/2109.14061/) (2021)
- [Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning](https://mlsanity.com/paper/2103.00833/) (2021)
- [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)
- [A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes](https://mlsanity.com/paper/2202.01487/) (2022)
- [Peer Collaborative Learning for Polyphonic Sound Event Detection](https://mlsanity.com/paper/2110.03511/) (2021)
- [Formula-Supervised Sound Event Detection: Pre-Training Without Real Data](https://mlsanity.com/paper/2504.04428/) (2025)
- [Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding](https://mlsanity.com/paper/2607.04383/) (2026)
- [MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Training](https://mlsanity.com/paper/2609.15221/) (2026)

## Abstract (arXiv)

This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale detection of sound events using weakly labeled data (without time boundaries). The target of the systems is to provide not only the event class but also the event time boundaries given that multiple events can be present in an audio recording. Another challenge of the task is to explore the possibility to exploit a large amount of unbalanced and unlabeled training data together with a small weakly labeled training set to improve system performance. The data are Youtube video excerpts from domestic context which have many applications such as ambient assisted living. The domain was chosen due to the scientific challenges (wide variety of sounds, time-localized events.. .) and potential industrial applications .