README.md

# SO-Bench: Spatial-Omni Benchmark for FOA Spatial Audio Question Answering

SO-Bench is an audio-only spatial question answering benchmark built from FOA spatial audio. Each example contains a first-order ambisonics (FOA) waveform and one natural-language question-answer pair. The questions cover sound event detection, localization, spatial relations, motion, and multi-step spatial reasoning.

SO-Bench is part of the [Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding](https://arxiv.org/abs/2606.10738).

## Dataset Contents

- **Audio format**: FOA waveform files (`.wav`)
- **Split**: `test`
- **QA metadata**: one JSON object per question-answer pair

## File Structure

```text
SO-Bench/
  README.md
  score_predictions_llm_judge.py    # LLM-judge scorer for predictions

  audio/
    test/
      foa_413d6010b124f7adcc0b.wav
      foa_be5d2301ea1330fce366.wav
      ...

  qa/
    test.jsonl
```

## Dataset Statistics
Bench sub-tasks is shown in figures below. 
<!-- figure -->
<img src="figures/bench_tasks.jpeg" alt="SO-Bench task distribution" width="400"/>

Task distribution:

| Task | Count |
|---|---:|
| `estimate_elevation` | 1,153 |
| `estimate_azimuth` | 1,152 |
| `detect_time` | 667 |
| `detect_source` | 667 |
| `identify_source_by_doa` | 667 |
| `identify_source_by_location` | 666 |
| `onset_from_location` | 487 |
| `estimate_distance` | 486 |
| `multi_hop` | 333 |
| `spatial_temporal` | 333 |
| `compare_elevation` | 333 |
| `relative_left_right` | 333 |
| `compare_distance` | 333 |
| `speech_content` | 171 |
| `classify_motion` | 62 |
| `count_sources` | 34 |

## QA Metadata Format

Each line in `qa/test.jsonl` is one QA example.

Example:

```json
{
  "qa_id": "qa_4e31e78e5a6f7579fe52",
  "split": "test",
  "audio_id": "foa_413d6010b124f7adcc0b",
  "audio_path": "audio/test/foa_413d6010b124f7adcc0b.wav",
  "task_name": "detect_time",
  "question_class": "detect",
  "answer_format": "structured_text",
  "question": "Listen to the audio clip and answer based only on what you hear. During which time interval can the sound of the gong be heard? Based on the audio clip, state which sound sources are audible and when each one occurs. Answer in one natural sentence and report times in seconds.",
  "answer": "The gong is audible from 18.5s to 20.0s.",
  "canonical_answer": "18.5s to 20.0s",
  "answer_meta": {"time_span": [18.5, 20.0]}
}
```

Important fields:

- `qa_id`: anonymized QA identifier. **Use this as the stable join key with your
  `predictions.jsonl`** (see "Benchmark Methods" below).
- `split`: dataset split. This release contains only `test`.
- `audio_id`: anonymized FOA audio identifier.
- `audio_path`: relative path to the FOA waveform.
- `task_name`: task type for the question.
- `question_class`: broad category such as `detect`, `azimuth`, `elevation`, `distance`, `motion`, or `reasoning`.
- `answer_format`: expected answer style.
- `question`: natural-language question.
- `answer`: full natural-language answer.
- `canonical_answer`: normalized answer target for evaluation or parsing.
- `answer_meta`: structured ground-truth used by the LLM-judge scorer.
  Populated for the numeric and time-grounded tasks; empty `{}` otherwise.
  Possible keys:
  - `azimuth_deg` (float, for `estimate_azimuth`)
  - `elevation_deg` (float, for `estimate_elevation`)
  - `distance_m` (float, for `estimate_distance`)
  - `time_span` (`[start_s, end_s]`, for `detect_time`, `spatial_temporal`,
    and single-event `detect_source`)
  - `onset_time` (float, for `onset_from_location`)
  - `active_count` (int, for `count_sources`)

## Download

Install the Hugging Face CLI:

```bash
pip install -U "huggingface_hub[cli]"
```

Download the full benchmark:

```bash
hf download dieKarotte/SO-Bench \
  --repo-type dataset \
  --local-dir SO-Bench
```

Download only the QA metadata:

```bash
hf download dieKarotte/SO-Bench \
  --repo-type dataset \
  --local-dir SO-Bench \
  --include "qa/test.jsonl" \
  --include "README.md"
```

## Benchmark Methods

After running your model on every entry in `qa/test.jsonl` and writing the
outputs to a `predictions.jsonl` file, score them with the bundled
`score_predictions_llm_judge.py`. The scorer normalizes each model output via
an OpenAI-compatible chat-completions API, then applies task-specific local
metrics (numeric thresholds, IoU, exact-match, WER) to the normalized fields.

### `predictions.jsonl` schema

One JSON object per line, in the **same order** as `qa/test.jsonl` (or include
`qa_id` so the scorer can match by id, which is the recommended path):

```json
{
  "qa_id": "qa_4e31e78e5a6f7579fe52",
  "audio_path": "audio/test/foa_413d6010b124f7adcc0b.wav",
  "task_name": "detect_time",
  "prediction": "The gong is heard from 18.4s to 20.0s."
}
```

The scorer reads the model output from `prediction` (preferred),
`prediction_cleaned`, or `prediction_raw`, in that order.

### Running the scorer

```bash
export OPENAI_API_KEY=sk-...                          # or pass --api-key
# export OPENAI_BASE_URL=https://api.openai.com/v1    # optional, override base URL

python3 score_predictions_llm_judge.py \
  --predictions-jsonl your_predictions_file.jsonl \
  --qa-root /path/to/SO-Bench/qa \
  --split test \
  --model gpt-4o \
  --concurrency 256 \
  --force-refresh \
  --require-api \
  --output-json /tmp/llm_result.json \
  --judged-jsonl /tmp/llm_judged_records.jsonl \
  --cache-jsonl /tmp/llm_judge_cache.jsonl
```

### Scoring rules per task

- **Numeric thresholds** (binary correctness):
  - `estimate_azimuth`: wrap-around angular error ≤ `--angle-threshold-deg` (default **20°**).
  - `estimate_elevation`: absolute error ≤ `--elevation-threshold-deg` (default **10°**).
  - `estimate_distance`: absolute error ≤ `--distance-threshold-m` (default **1.0 m**).
    Note: the prompt's `near <1.5 m / mid-range 1.5–4.0 m / far >4.0 m` buckets
    are textual hints to the model; the scorer judges by absolute MAE, not bucket
    membership.
  - `onset_from_location`: absolute error ≤ `--time-threshold-s` (default **0.2 s**).
  - `count_sources`: exact integer match.
- **IoU**: `detect_time` returns time-span IoU as the score directly.
- **Semantic source match (LLM-judged)**: `detect_source`,
  `identify_source_by_doa`, `identify_source_by_location`, `multi_hop`. Correct
  iff the LLM judge returns `exact_or_synonym` (or `compatible_but_broader`
  when `--accept-broader-source` is set).
  - `detect_source` defaults to `--detect-source-time-policy event_only` (label
    only). Pass `event_times_iou` to multiply the score by the time-span IoU
    when both spans exist.
- **Relation match (LLM-judged)**: `compare_distance`, `compare_elevation`,
  `relative_left_right`. Correct iff the LLM judge returns
  `relation_match=true`.
- **Spatial-temporal**: `spatial_temporal` defaults to
  `--spatial-temporal-time-policy semantic_times_iou`: `(source_match ∧
  direction_match) × IoU(time_span)`. Pass `semantic_only` to drop the IoU
  factor.
- **Motion**: `classify_motion` correct iff `motion_match=true`.
- **Speech transcript (local, no LLM call)**: `speech_content` is scored
  locally via word error rate against `canonical_answer`, with binary
  correctness threshold WER ≤ 0.5. The scorer also reports mean / median WER
  and the fraction at WER ≤ 0.3 / 0.5 / 1.0.

## 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.

```text
@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},
}
```