---
license: cc-by-4.0
pretty_name: V33DA
task_categories:
- audio-classification
- video-classification
tags:
- animal-behavior
- bioacoustics
- bird-vocalization
- multimodal
- 3d-pose
- localization
- attribution
- radio-telemetry
- accelerometer
- zebra-finch
- benchmark
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: v33da-*.parquet
---

# Who Called? V33DA: A Physically Verified Multimodal Benchmark for Vocal Attribution in Zebra Finch Groups

**Task.** Given a detected zebra finch vocalization and the set of birds visible at that moment, determine which bird produced the call. Caller identity is verified physically from on-body accelerometer vibration; the accelerometer channel is withheld from benchmark models and used only by an oracle ceiling.

V33DA provides 33,625 verified vocalization events from 10 individually identified zebra finches across 3 experiments (2021–2023), each with synchronized 5-channel audio, multi-view video, calibrated 3D pose for every visible candidate, per-bird FM radio telemetry, and accelerometer-derived ground-truth labels.

| | BP01 | BP02 | BP05 | Total |
|---|---|---|---|---|
| Birds | 4 | 3 | 3 | 10 |
| Events | 16,219 | 11,577 | 5,829 | 33,625 |
| Recording days | 10 | 3 | 3 | 16 |
| Raw hours | ~46h | ~14h | ~14h | ~74h |

**Code:** [github.com/marisbasha/v33da](https://github.com/marisbasha/v33da) &nbsp;·&nbsp; **Companion release:** [songbirdini/v33da_pp](https://huggingface.co/datasets/songbirdini/v33da_pp) (V33DA++: overlap and longer-context buckets)

## Quick start

```bash
pip install huggingface_hub
huggingface-cli download songbirdini/V33DA --repo-type dataset --local-dir data/v33da
```

Or in Python:

```python
from huggingface_hub import snapshot_download
snapshot_download("songbirdini/V33DA", repo_type="dataset", local_dir="data/v33da")
```

### Loading a sample

Each binary array column is serialized with `numpy.save`. To decode:

```python
import io
import numpy as np
import pyarrow.parquet as pq

table = pq.read_table("data/v33da/v33da-00000.parquet")
row = table.to_pydict()

# 3D keypoints: (T_frames, N_birds, 5, 3) float32
kp3d = np.load(io.BytesIO(row["keypoints_3d"][0]))

# Audio path -> multichannel WAV
audio_path = row["audio_path"][0]  # e.g. "audio/juvExpBP01/2021-06-28/..."
```

The included `explore.ipynb` notebook shows how to inspect audio, video, pose, radio, and accelerometer data, and how to reproject 3D keypoints into 2D camera views using the shipped calibrations.

## Modalities

Each parquet row is one vocalization event:

| Column | Description |
|---|---|
| `audio_path` | 5-channel cage-microphone WAV (24,414 Hz, float32) |
| `accelerometer_path` | Per-bird accelerometer WAV (oracle verification signal) |
| `video_path` | Aligned composite MP4 clip (3 views, 47.68 fps) |
| `keypoints_3d` | Triangulated 3D pose, shape `(T_frames, N_birds, 5, 3)` float32: 5 keypoints per bird (beak, head, backpack, tail base, tail tip) |
| `keypoints_2d_top` | 2D keypoints in top-view image space, shape `(T_frames, N_birds, 5, 2)` float32 |
| `keypoints_2d_back` | 2D keypoints in back-view image space, shape `(T_frames, N_birds, 5, 2)` float32 |
| `radio_*` | 21 per-bird FM radio telemetry signals per frame |
| `vocalizer_idx` | Ground-truth caller index |
| `bird_color` | Color identity of the vocalizer |
| `experiment` | Experiment name (juvExpBP01 / juvExpBP02 / juvExpBP05) |
| `date` | Recording date |

## Ground-truth labels

Labels come from **on-body accelerometer vibration**, not from microphone-based localization. A WhisperSeg model proposes candidate windows from the demodulated signal; events are retained only when audible in microphones, showing characteristic on-body vibration, with no overlapping call from another bird. All candidates are manually reviewed.

### Filtering

From 39,329 reviewed candidates:
- 1,768 rejected as invalid during manual review
- 3,784 removed because another bird vocalized during the same window (overlap filtering)
- 152 removed by 3D-to-2D reprojection error (> 40 px)

Yielding **33,625 released events**.

## Per-bird counts

| Experiment | Bird | Events |
|---|---|---|
| BP01 | blue | 5,735 |
| BP01 | peach | 2,277 |
| BP01 | red | 3,963 |
| BP01 | white | 4,244 |
| BP02 | blue | 4,923 |
| BP02 | peach | 4,589 |
| BP02 | red | 2,065 |
| BP05 | brown | 56 |
| BP05 | purple | 2,140 |
| BP05 | yellow | 3,633 |

> **Note on bird colors.** Backpack color codes are experiment-local identifiers, not global bird IDs. A color that appears in two experiments (e.g. `red` in BP01 and BP02) refers to two different physical individuals; color-to-bird assignment is fixed within an experiment. All 10 released birds are distinct physical individuals; no bird participates in more than one experiment.

## Release contents

| Path | Description |
|---|---|
| `v33da-*.parquet` | 23 pooled parquet shards |
| `audio/` | Multichannel microphone WAV files |
| `accelerometer/` | Multichannel accelerometer WAV files |
| `clips/` | Aligned composite MP4 clips |
| `calibrations/` | Per-experiment camera calibration files |
| `explore.ipynb` | Dataset exploration notebook |
| `metadata.json` | Release schema and filter metadata |

## Evaluation regimes

The [benchmark code](https://github.com/marisbasha/v33da) supports three evaluation regimes:

| Regime | What transfers? | Use case |
|---|---|---|
| **Session-disjoint** | Same birds, different days | In-domain ceiling |
| **Held-out experiment** | New birds, new year, new cage | Cross-cohort transfer |
| **Leave-one-bird-out** | One unseen bird per fold | Identity-shift stress test |

## Limitations

V33DA is collected in one aviary with one recording geometry across three experiments. Cross-experiment evaluation combines bird-identity shift, group-composition shift, and recording-date shift. The released benchmark excludes overlapping vocalizations and filters out samples with poor geometric consistency. Overlap-filtered calls and longer-context windows are released separately through the companion dataset [songbirdini/v33da_pp](https://huggingface.co/datasets/songbirdini/v33da_pp).

## Citation

If you use V33DA, please cite:

```bibtex
@unpublished{basha2026v33da,
  title  = {Who Called? V33DA: A Physically Verified Multimodal Benchmark for Vocal Attribution in Zebra Finch Groups},
  author = {Basha, Maris and Wang, Yuhang and Chen, Xiaoran and Cheng, Longbiao and Yapura, Luca and Zai, Anja T. and Salzmann, Mathieu and Hahnloser, Richard},
  year   = {2026},
  note   = {Under review},
}
```