# STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events

arXiv: https://arxiv.org/abs/2206.01948 · HTML: https://mlsanity.com/paper/2206.01948/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2022.

## Built on by (most cited first, 16 of 16)

- [STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events](https://mlsanity.com/paper/2306.09126/) (2023)
- [Seeing Sound, Hearing Sight: Uncovering Modality Bias and Conflict of AI models in Sound Localization](https://mlsanity.com/paper/2505.11217/) (2025)
- [Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding](https://mlsanity.com/paper/2606.10738/) (2026)
- [TF-Mamba: A Time-Frequency Network for Sound Source Localization](https://mlsanity.com/paper/2409.05034/) (2024)
- [Sci-Phi: A Large Language Model Spatial Audio Descriptor](https://mlsanity.com/paper/2510.05542/) (2025)
- [Not in Sync: Unveiling Temporal Bias in Audio Chat Models](https://mlsanity.com/paper/2510.12185/) (2025)
- [Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification](https://mlsanity.com/paper/2507.12042/) (2025)
- [Leveraging Reverberation and Visual Depth Cues for Sound Event Localization and Detection with Distance Estimation](https://mlsanity.com/paper/2410.22271/) (2024)
- [MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal Domains for Sound Event Localization and Detection](https://mlsanity.com/paper/2406.08771/) (2024)
- [A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods](https://mlsanity.com/paper/2507.01143/) (2025)
- [Auditory Intelligence: Understanding the World Through Sound](https://mlsanity.com/paper/2508.07829/) (2025)
- [DroneAudioset: An Audio Dataset for Drone-based Search and Rescue](https://mlsanity.com/paper/2510.15383/) (2025)
- [SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios](https://mlsanity.com/paper/2607.02343/) (2026)
- [CoLoC: Conditioned Localizer and Classifier for Sound Event Localization and Detection](https://mlsanity.com/paper/2210.13932/) (2022)
- [DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection](https://mlsanity.com/paper/2410.22803/) (2024)
- [Supervising Sound Localization by In-the-wild Egomotion](https://mlsanity.com/paper/2610.01388/) (2026)

## Abstract (arXiv)

This report presents the Sony-TAu Realistic Spatial Soundscapes 2022 (STARS22) dataset for sound event localization and detection, comprised of spatial recordings of real scenes collected in various interiors of two different sites. The dataset is captured with a high resolution spherical microphone array and delivered in two 4-channel formats, first-order Ambisonics and tetrahedral microphone array. Sound events in the dataset belonging to 13 target sound classes are annotated both temporally and spatially through a combination of human annotation and optical tracking. The dataset serves as the development and evaluation dataset for the Task 3 of the DCASE2022 Challenge on Sound Event Localization and Detection and introduces significant new challenges for the task compared to the previous iterations, which were based on synthetic spatialized sound scene recordings. Dataset specifications are detailed including recording and annotation process, target classes and their presence, and details on the development and evaluation splits. Additionally, the report presents the baseline system that accompanies the dataset in the challenge with emphasis on the differences with the baseline of the previous iterations; namely, introduction of the multi-ACCDOA representation to handle multiple simultaneous occurences of events of the same class, and support for additional improved input features for the microphone array format. Results of the baseline indicate that with a suitable training strategy a reasonable detection and localization performance can be achieved on real sound scene recordings. The dataset is available in https://zenodo.org/record/6387880.