Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding
Paper • 2606.10738 • Published • 1
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End of preview.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
SO-Dataset: Spatial FOA Audio Dataset
SO-Dataset is a large-scale spatial audio dataset in first-order ambisonics (FOA) format. Each example contains FOA waveform and spatial event annotations in DCASE-style CSV files. The dataset combines simulated spatial scenes and real FOA recordings, and all sound event labels are mapped into a unified 63-class sound event taxonomy based on the FSD50k dataset.
The public release stores audio and annotations as tar shards. The tar files preserve the same relative paths used by the metadata, so extracting the archives recreates the audio/ and annotations/ directories expected by the JSONL files.
Dataset Contents
- Audio format: FOA waveform files (
.wav) - Spatial annotations: DCASE-style CSV files
- Labels: unified FSD50k label set
- Splits:
train,valid,test - Metadata: one JSON object per audio scene
- Packaging: path-preserving tar shards for easier download and upload
File Structure
SO-Dataset/
label_mapping.json
tar_shard_summary.json
metadata/
train.jsonl
valid.jsonl
test.jsonl
manifests/
audio-train.jsonl
audio-valid.jsonl
audio-test.jsonl
annotations-train.jsonl
annotations-valid.jsonl
annotations-test.jsonl
archives/
audio/
train/
audio-train-000000.tar
audio-train-000001.tar
...
valid/
test/
annotations/
train/
annotations-train-000000.tar
valid/
annotations-valid-000000.tar
test/
annotations-test-000000.tar
After extraction, the archives create:
audio/{train,valid,test}/*.wav
annotations/{train,valid,test}/*.csv
These paths match the paths stored in metadata/*.jsonl.
Dataset Statistics
SO-Dataset contains 400K FOA audio segments across 233 scenes, with a total of 1.27M annotated sound events.
The dataset and annotations details are shown in the following figure. Figure(a) shows the sub-tasks in SO-QA and SO-Bench, including Detection and Localization, Spatial Relation Understanding, and Complex Reasoning with Semantics. Figure(b) shows the data source of sound events in the dataset. Figure(c) shows the building process of the dataset, including the recording, simulation and collect subset. After building the SO-Dataset, we generate QA pairs and build SO-QA using the metadata of SO-Dataset. Figure(d) shows the distribution of spatial event in our dataset, including azimuth, elevation and distance.
Metadata Format
Each line in metadata/{split}.jsonl is a JSON object describing one FOA scene.
Example:
{
"schema_version": "spatial_foa_scene_v1",
"split": "train",
"dataset": "sim_static",
"data_source": "sim_static",
"scene_id": "train/ov2_000000",
"audio": {
"duration_seconds": 20.0,
"foa_path": "audio/train/foa_fed1992f629ae5f3db28.wav"
},
"scene_annotation_csv_path": "annotations/train/foa_fed1992f629ae5f3db28.csv",
"sources": [
{
"source_id": "1",
"track_id": 0,
"original_label": "telephone_alarm",
"label": "telephone_alarm",
"label_id": 50,
"active_duration_seconds": 19.999937,
"active_times": [[0.000063, 20.0]],
"source_trajectory_csv_path": "annotations/train/foa_fed1992f629ae5f3db28_src00.csv",
"motion": {
"is_moving": false,
"pattern": null,
"description": null,
"scene_description": null
}
}
]
}
Important fields:
audio.foa_path: relative path to the FOA waveform after extraction.audio.duration_seconds: audio duration in seconds.scene_annotation_csv_path: combined scene-level DCASE-style CSV annotation.sources: list of individual sound sources in the scene.sources[*].source_trajectory_csv_path: per-source trajectory CSV.sources[*].original_label: label before mapping.sources[*].labelandsources[*].label_id: final FSD63 label and class id.sources[*].active_times: one or more[start_seconds, end_seconds]intervals.sources[*].track_id: source/event track id used in the CSV annotation.
Label Mapping
label_mapping.json defines the sound event label space:
class_count:63class_names: ordered class namesclass_name_to_id: class name to integer idclass_id_to_name: integer id to class name
Use label_mapping.json as the canonical class-id definition.
The following figure shows the distribution of the 63 classes in our dataset.
Download
Install the Hugging Face CLI:
pip install -U "huggingface_hub[cli]"
Download the full dataset:
hf download dieKarotte/SO-Dataset \
--repo-type dataset \
--local-dir SO-Dataset
Download only metadata, manifests, and the label mapping:
hf download dieKarotte/SO-Dataset \
--repo-type dataset \
--local-dir SO-Dataset \
--include "metadata/*" \
--include "manifests/*" \
--include "label_mapping.json" \
--include "tar_shard_summary.json"
Download only the training audio shards:
hf download dieKarotte/SO-Dataset \
--repo-type dataset \
--local-dir SO-Dataset \
--include "archives/audio/train/*" \
--include "metadata/train.jsonl" \
--include "label_mapping.json"
Extraction
Extract all audio and annotation shards from the dataset root:
cd SO-Dataset
find archives/audio -name "*.tar" -print0 | xargs -0 -n 1 -P 4 tar -xf
find archives/annotations -name "*.tar" -print0 | xargs -0 -n 1 -P 4 tar -xf
Extract only the training split:
cd SO-Dataset
find archives/audio/train -name "*.tar" -print0 | xargs -0 -n 1 -P 4 tar -xf
find archives/annotations/train -name "*.tar" -print0 | xargs -0 -n 1 -P 4 tar -xf
After extraction, metadata paths are directly usable:
audio/train/foa_....wav
annotations/train/foa_....csv
annotations/train/foa_...._src00.csv
Manifests
The files in manifests/ list the tar shards for each group and split.
Example row:
{
"kind": "audio",
"split": "train",
"shard": "archives/audio/train/audio-train-000000.tar",
"files": 1741,
"payload_bytes": 4999665664,
"estimated_tar_bytes": 5000501760
}
Video
As mentioned in the paper, we are currently in the process of anonymizing and preparing the video data for release. This involves removing any personally identifiable information and ensuring that all privacy concerns are addressed. We will make the video data available as soon as it has been processed and organized, so please stay tuned for updates.
Citation and License
Please cite this dataset as appropriate for your use. If you redistribute or use the dataset in downstream work, make sure your usage is compatible with the licenses of the underlying audio and spatial data sources.
@misc{zhu2026spatialomnispatialaudiounderstanding,
title={Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding},
author={Zhiyuan Zhu and Yixuan Chen and Yiwen Shao and Wenxiang Guo and Changhao Pan and Yu Zhang and Yuxiang Wang and Wei Liu and Houhua Zhang and Chengkuan Zeng and Wenbo Cheng and Yunxi Liu and Rui Yang and Steve Yves and Liefeng Bo and Zhou Zhao},
year={2026},
eprint={2606.10738},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2606.10738},
}
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