# DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels

arXiv: https://arxiv.org/abs/2406.08056 · HTML: https://mlsanity.com/paper/2406.08056/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2024.

## Built on by (most cited first, 8 of 8)

- [Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries](https://mlsanity.com/paper/2507.16343/) (2025)
- [Not in Sync: Unveiling Temporal Bias in Audio Chat Models](https://mlsanity.com/paper/2510.12185/) (2025)
- [Prototype based Masked Audio Model for Self-Supervised Learning of Sound Event Detection](https://mlsanity.com/paper/2409.17656/) (2024)
- [CASTELLA: Long Audio Dataset with Captions and Temporal Boundaries](https://mlsanity.com/paper/2511.15131/) (2025)
- [Energy Consumption Trends in Sound Event Detection Systems](https://mlsanity.com/paper/2409.08763/) (2024)
- [Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification](https://mlsanity.com/paper/2504.03329/) (2025)
- [RealDESED: A Real-World Domestic Sound Event Detection Benchmark](https://mlsanity.com/paper/2607.16736/) (2026)
- [MTDA-HSED: Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detection](https://mlsanity.com/paper/2409.06196/) (2024)

## Abstract (arXiv)

The Detection and Classification of Acoustic Scenes and Events Challenge Task 4 aims to advance sound event detection (SED) systems in domestic environments by leveraging training data with different supervision uncertainty. Participants are challenged in exploring how to best use training data from different domains and with varying annotation granularity (strong/weak temporal resolution, soft/hard labels), to obtain a robust SED system that can generalize across different scenarios. Crucially, annotation across available training datasets can be inconsistent and hence sound labels of one dataset may be present but not annotated in the other one and vice-versa. As such, systems will have to cope with potentially missing target labels during training. Moreover, as an additional novelty, systems will also be evaluated on labels with different granularity in order to assess their robustness for different applications. To lower the entry barrier for participants, we developed an updated baseline system with several caveats to address these aforementioned problems. Results with our baseline system indicate that this research direction is promising and is possible to obtain a stronger SED system by using diverse domain training data with missing labels compared to training a SED system for each domain separately.