# Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019

arXiv: https://arxiv.org/abs/2009.02792 · HTML: https://mlsanity.com/paper/2009.02792/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2020.

## Built on by (most cited first, 30 of 31)

- [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)
- [Sound Event Detection and Localization with Distance Estimation](https://mlsanity.com/paper/2403.11827/) (2024)
- [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)
- [Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation](https://mlsanity.com/paper/2204.01851/) (2022)
- [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)
- [Enhanced Sound Event Localization and Detection in Real 360-degree audio-visual soundscapes](https://mlsanity.com/paper/2401.17129/) (2024)
- [Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation](https://mlsanity.com/paper/2309.15938/) (2023)
- [Semantic Audio-Visual Navigation in Continuous Environments](https://mlsanity.com/paper/2603.19660/) (2026)
- [Locate This, Not That: Class-Conditioned Sound Event DOA Estimation](https://mlsanity.com/paper/2203.04197/) (2022)
- [Sound event localization and classification using WASN in Outdoor Environment](https://mlsanity.com/paper/2403.20130/) (2024)
- [Learning Multi-Target TDOA Features for Sound Event Localization and Detection](https://mlsanity.com/paper/2408.17166/) (2024)
- [Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features](https://mlsanity.com/paper/2411.03172/) (2024)
- [HARP: A Large-Scale Higher-Order Ambisonic Room Impulse Response Dataset](https://mlsanity.com/paper/2411.14207/) (2024)
- [A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods](https://mlsanity.com/paper/2507.01143/) (2025)
- [Latent Acoustic Mapping for Direction of Arrival Estimation: A Self-Supervised Approach](https://mlsanity.com/paper/2507.07066/) (2025)
- [Auditory Intelligence: Understanding the World Through Sound](https://mlsanity.com/paper/2508.07829/) (2025)
- [SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios](https://mlsanity.com/paper/2607.02343/) (2026)
- [TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models](https://mlsanity.com/paper/2608.29999/) (2026)
- [What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis](https://mlsanity.com/paper/2107.10469/) (2021)
- [TASK3 DCASE2021 Challenge: Sound event localization and detection using squeeze-excitation residual CNNs](https://mlsanity.com/paper/2107.14561/) (2021)
- [Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection](https://mlsanity.com/paper/2110.06126/) (2021)
- [Feature Aggregation in Joint Sound Classification and Localization Neural Networks](https://mlsanity.com/paper/2310.19063/) (2023)
- [DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection](https://mlsanity.com/paper/2410.22803/) (2024)
- [GRAM: Spatial general-purpose audio representation models for real-world applications](https://mlsanity.com/paper/2506.00934/) (2025)
- [What Do Neural Networks Learn for TDOA Estimation? A Cross-Architecture Probing Study](https://mlsanity.com/paper/2606.22020/) (2026)
- [Before the Warning Comes Too Late: Incremental Phone-Scam Detection from Speech](https://mlsanity.com/paper/2609.20223/) (2026)
- [EquiSELD: Efficient training of equivariant sound event localization and detection networks](https://mlsanity.com/paper/2609.23156/) (2026)

## Abstract (arXiv)

Sound event localization and detection is a novel area of research that emerged from the combined interest of analyzing the acoustic scene in terms of the spatial and temporal activity of sounds of interest. This paper presents an overview of the first international evaluation on sound event localization and detection, organized as a task of the DCASE 2019 Challenge. A large-scale realistic dataset of spatialized sound events was generated for the challenge, to be used for training of learning-based approaches, and for evaluation of the submissions in an unlabeled subset. The overview presents in detail how the systems were evaluated and ranked and the characteristics of the best-performing systems. Common strategies in terms of input features, model architectures, training approaches, exploitation of prior knowledge, and data augmentation are discussed. Since ranking in the challenge was based on individually evaluating localization and event classification performance, part of the overview focuses on presenting metrics for the joint measurement of the two, together with a reevaluation of submissions using these new metrics. The new analysis reveals submissions that performed better on the joint task of detecting the correct type of event close to its original location than some of the submissions that were ranked higher in the challenge. Consequently, ranking of submissions which performed strongly when evaluated separately on detection or localization, but not jointly on both, was affected negatively.