---
dataset_name: EMID-Emotion-Matching
annotations_creators:
- expert-generated
language:
- en
license: cc-by-nc-sa-4.0
pretty_name: EMID Music ↔ Image Emotion Matching Pairs
tags:
- audio
- music
- image
- multimodal
- emotion
- contrastive-learning
task_categories:
- audio-classification
- image-classification
- visual-question-answering
---

# EMID-Emotion-Matching

`orrzohar/EMID-Emotion-Matching` is a derived dataset built on top of
the **Emotionally paired Music and Image Dataset (EMID)** from ECNU (`ecnu-aigc/EMID`).
It is designed for *music ↔ image emotion matching* with Qwen-Omni–style models.

Each example contains:

- `audio`: mono waveform stored as `datasets.Audio` (HF Hub preview can play it)
- `sampling_rate`: sampling rate used when decoding (typically 16 kHz)
- `image`: a single image (`datasets.Image`)
- `same`: `bool`, whether the audio and image are labeled with the **same** emotion
- `emotion`: normalized image emotion tag (e.g. `amusement`, `excitement`) for positive pairs; empty string for negatives
- `question`: natural-language question used to prompt the model (several templates are mixed)
- `answer`: canonical supervision text (`yes - {emotion}` for positives, `no` for negatives)

| column         | type                            | description |
| -------------- | ------------------------------- | ----------- |
| `audio`        | `datasets.Audio (16k mono)`     | decoded waveform; HF UI can play it |
| `sampling_rate`| `int32`                         | explicit sample rate mirrored beside the `Audio` column |
| `image`        | `datasets.Image`                | PIL.Image-compatible object |
| `same`         | `bool`                          | `True` if the pair is emotion-aligned |
| `emotion`      | `string`                        | normalized emotion label for positives, `""` otherwise |
| `question`     | `string`                        | user prompt template |
| `answer`       | `string`                        | canonical supervision text (`yes - {emotion}` / `no`) |

The original EMID row has one music clip and up to **three** tagged images
(`Image1`, `Image2`, `Image3`). For each `(audio, image)` pair we create:

- **1 positive example**: the audio and its own tagged image (`same = True`, `emotion = image_tag`)
- **NEGATIVES_PER_POSITIVE = 1 negative example**: the same audio paired with an image drawn
  from a *different* emotion tag (`same = False`, `emotion = ""`)

With `MAX_SOURCE_ROWS = 4000`, this yields ~24,000 examples (positives + negatives),
which we then split into:

- `train`: 19,200 examples
- `test`: 4,800 examples

## Source Data (EMID)

The base EMID dataset is described in:

- **Emotionally paired Music and Image Dataset (EMID)**  
  *Y. Guo, J. Li, et al.*  
  arXiv:2308.07622 — "Emotionally paired Music and Image Dataset (EMID)"  
  <https://arxiv.org/abs/2308.07622>

EMID contains 10,738 unique music clips, each paired with three images in the same
emotional category, plus rich annotations:

- `Audio_Filename`: unique filename of the music clip
- `genre`: letter A–M, one of 13 emotional categories
- `feeling`: distribution of free-form feelings reported by listeners (% per feeling)
- `emotion`: ratings on 11 emotional dimensions (1–9)
- `Image{1,2,3}_filename`: matched image filenames
- `Image{1,2,3}_tag`: image emotion category (e.g. `amusement`, `excitement`)
- `Image{1,2,3}_text`: GIT-generated captions
- `is_original_clip`: whether this is an original or expanded clip

For more details, see the EMID README and the paper above.

## How This Derived Dataset Was Built

The script `prepare_emid_pairs.py` performs the following steps offline:

1. Load `ecnu-aigc/EMID` (train split) and decode:
   - `Audio_Filename` with `Audio(decode=True)`
   - `Image{1,2,3}_filename` with `datasets.Image(decode=True)`
2. Optionally cap the number of source rows with `MAX_SOURCE_ROWS` (default 4000).
3. Build an **image pool** keyed by normalized emotion tags.
4. For each EMID row and each available image (up to 3 per row):
   - Create a positive pair `(audio, image, same=True, emotion=image_tag)`.
   - Sample `NEGATIVES_PER_POSITIVE` images from *different* emotion tags to form negatives.
5. Normalize the emotion strings (lowercase, replace spaces and punctuation with `_`).
6. Draw a random question from a small set of Qwen-style templates and attach it as `question`.
7. Store the mono waveform as `datasets.Audio` and the image as `datasets.Image` so
   that downstream scripts can call `datasets.load_dataset` without extra decoding logic.
8. Split into train/test with `TRAIN_FRACTION = 0.8`.

This yields a simple, flat structure that is convenient for SFT / contrastive training
with Qwen2.5-Omni (or other multimodal LMs), without re-doing negative sampling or
audio/image decoding inside notebooks.

## Suggested Usage

```python
from datasets import load_dataset

ds = load_dataset("orrzohar/EMID-Emotion-Matching")
train_ds = ds["train"]
test_ds = ds["test"]

ex = train_ds[0]
audio = ex["audio"]        # dict with "array" + "sampling_rate"
sr = ex["sampling_rate"]   # int
image = ex["image"]        # PIL.Image.Image
same = ex["same"]          # bool
emotion = ex["emotion"]    # str
question = ex["question"]  # str
answer = ex["answer"]      # str
```

In the Qwen-Omni demos, we typically:

- Use `question` as the user prompt,
- Provide `audio` and `image` as multimodal inputs, and
- Supervise the model with the provided `answer` (or regenerate your own phrasing from `same`/`emotion`).

## License

This derived dataset **inherits the license** from EMID:

- **CC BY-NC-SA 4.0** (Attribution–NonCommercial–ShareAlike 4.0 International)

You **must**:

- Use the data only for **non-commercial** purposes.
- Provide appropriate **attribution** to the EMID authors and this derived dataset.
- Distribute derivative works under the **same license**.

Please refer to the full license text for details:
<https://creativecommons.org/licenses/by-nc-sa/4.0/>

If you use this dataset in academic work, please cite the EMID paper and, if appropriate,
this derived dataset as well.