# A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection

arXiv: https://arxiv.org/abs/2106.06999 · HTML: https://mlsanity.com/paper/2106.06999/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2021.

## 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)
- [Sound Event Detection and Localization with Distance Estimation](https://mlsanity.com/paper/2403.11827/) (2024)
- [Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning](https://mlsanity.com/paper/2308.09514/) (2023)
- [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)
- [Locate This, Not That: Class-Conditioned Sound Event DOA Estimation](https://mlsanity.com/paper/2203.04197/) (2022)
- [LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy](https://mlsanity.com/paper/2302.09409/) (2023)
- [A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods](https://mlsanity.com/paper/2507.01143/) (2025)
- [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)
- [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)
- [Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection](https://mlsanity.com/paper/2110.06126/) (2021)
- [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)
- [Bearings: Self-Supervised Soundfield Embeddings from First-Order Ambisonics](https://mlsanity.com/paper/2609.23152/) (2026)
- [EquiSELD: Efficient training of equivariant sound event localization and detection networks](https://mlsanity.com/paper/2609.23156/) (2026)
- [Supervising Sound Localization by In-the-wild Egomotion](https://mlsanity.com/paper/2610.01388/) (2026)
- [SonicWorld: Source-Centric Latent Dynamics for Spatial Audio Reasoning](https://mlsanity.com/paper/2610.17131/) (2026)

## Abstract (arXiv)

This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD). The dataset is based on emulation of real recordings of static or moving sound events under real conditions of reverberation and ambient noise, using spatial room impulse responses captured in a variety of rooms and delivered in two spatial formats. The acoustical synthesis remains the same as in the previous iteration of the challenge, however the new dataset brings more challenging conditions of polyphony and overlapping instances of the same class. The most important difference of the new dataset is the introduction of directional interferers, meaning sound events that are localized in space but do not belong to the target classes to be detected and are not annotated. Since such interfering events are expected in every real-world scenario of SELD, the new dataset aims to promote systems that deal with this condition effectively. A modified SELDnet baseline employing the recent ACCDOA representation of SELD problems accompanies the dataset and it is shown to outperform the previous one. The new dataset is shown to be significantly more challenging for both baselines according to all considered metrics. To investigate the individual and combined effects of ambient noise, interferers, and reverberation, we study the performance of the baseline on different versions of the dataset excluding or including combinations of these factors. The results indicate that by far the most detrimental effects are caused by directional interferers.