# A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection

arXiv: https://arxiv.org/abs/2006.01919 · HTML: https://mlsanity.com/paper/2006.01919/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 21 of 21)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events](https://mlsanity.com/paper/2306.09126/) (2023)
- [L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment](https://mlsanity.com/paper/2202.10372/) (2022)
- [L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing](https://mlsanity.com/paper/2104.05499/) (2021)
- [Diff-SAGe: End-to-End Spatial Audio Generation Using Diffusion Models](https://mlsanity.com/paper/2410.11299/) (2024)
- [Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification](https://mlsanity.com/paper/2507.12042/) (2025)
- [DCASE 2021 Task 3: Spectrotemporally-aligned Features for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2106.15190/) (2021)
- [Leveraging Reverberation and Visual Depth Cues for Sound Event Localization and Detection with Distance Estimation](https://mlsanity.com/paper/2410.22271/) (2024)
- [Spatio-Temporal Audio Language Modeling for Dynamic Sound Sources](https://mlsanity.com/paper/2606.14141/) (2026)
- [HARP: A Large-Scale Higher-Order Ambisonic Room Impulse Response Dataset](https://mlsanity.com/paper/2411.14207/) (2024)
- [Spatial and Semantic Embedding Integration for Stereo Sound Event Localization and Detection in Regular Videos](https://mlsanity.com/paper/2507.04845/) (2025)
- [Integrating Spatial and Semantic Embeddings for Stereo Sound Event Localization in Videos](https://mlsanity.com/paper/2509.06598/) (2025)
- [SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios](https://mlsanity.com/paper/2607.02343/) (2026)
- [Unsupervised Discriminative Learning of Sounds for Audio Event Classification](https://mlsanity.com/paper/2105.09279/) (2021)
- [What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis](https://mlsanity.com/paper/2107.10469/) (2021)
- [Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sorensen-Dice Coefficient Loss and Transfer Learning](https://mlsanity.com/paper/2107.10471/) (2021)
- [TASK3 DCASE2021 Challenge: Sound event localization and detection using squeeze-excitation residual CNNs](https://mlsanity.com/paper/2107.14561/) (2021)
- [An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation](https://mlsanity.com/paper/2501.10755/) (2025)
- [GRAM: Spatial general-purpose audio representation models for real-world applications](https://mlsanity.com/paper/2506.00934/) (2025)
- [ToS: A Team of Specialists ensemble framework for Stereo Sound Event Localization and Detection with distance estimation in Video](https://mlsanity.com/paper/2601.17611/) (2026)
- [Supervising Sound Localization by In-the-wild Egomotion](https://mlsanity.com/paper/2610.01388/) (2026)

## Abstract (arXiv)

This report presents the dataset and the evaluation setup of the Sound Event Localization & Detection (SELD) task for the DCASE 2020 Challenge. The SELD task refers to the problem of trying to simultaneously classify a known set of sound event classes, detect their temporal activations, and estimate their spatial directions or locations while they are active. To train and test SELD systems, datasets of diverse sound events occurring under realistic acoustic conditions are needed. Compared to the previous challenge, a significantly more complex dataset was created for DCASE 2020. The two key differences are a more diverse range of acoustical conditions, and dynamic conditions, i.e. moving sources. The spatial sound scenes are created using real room impulse responses captured in a continuous manner with a slowly moving excitation source. Both static and moving sound events are synthesized from them. Ambient noise recorded on location is added to complete the generation of scene recordings. A baseline SELD method accompanies the dataset, based on a convolutional recurrent neural network, to provide benchmark scores for the task. The baseline is an updated version of the one used in the previous challenge, with input features and training modifications to improve its performance.