# DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection

arXiv: https://arxiv.org/abs/2102.08833 · HTML: https://mlsanity.com/paper/2102.08833/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [The Non-IID Data Quagmire of Decentralized Machine Learning](https://mlsanity.com/paper/1910.00189/) (2019)
- [Protection Against Reconstruction and Its Applications in Private Federated Learning](https://mlsanity.com/paper/1812.00984/) (2018)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification](https://mlsanity.com/paper/1907.01803/) (2019)
- [Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks](https://mlsanity.com/paper/1905.05928/) (2019)
- [End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input](https://mlsanity.com/paper/1805.03647/) (2018)
- [Sound Event Detection with Sequentially Labelled Data Based on Connectionist Temporal Classification and Unsupervised Clustering](https://mlsanity.com/paper/1904.12102/) (2019)

## Built on by (most cited first, 3 of 3)

- [SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes](https://mlsanity.com/paper/2506.12222/) (2025)
- [Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data](https://mlsanity.com/paper/2105.04727/) (2021)
- [Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification](https://mlsanity.com/paper/2509.24901/) (2025)

## Abstract (arXiv)

Research on sound event detection (SED) in environmental settings has seen increased attention in recent years. The large amounts of (private) domestic or urban audio data needed raise significant logistical and privacy concerns. The inherently distributed nature of these tasks, make federated learning (FL) a promising approach to take advantage of largescale data while mitigating privacy issues. While FL has also seen increased attention recently, to the best of our knowledge there is no research towards FL for SED. To address this gap and foster further research in this field, we create and publish novel FL datasets for SED in domestic and urban environments. Furthermore, we provide baseline results on the datasets in a FL context for three deep neural network architectures. The results indicate that FL is a promising approach for SED, but faces challenges with divergent data distributions inherent to distributed client edge devices.