task_categories:audio-classification size_categories:n<1K language:zh language:en license:mit music art region:us
Music Emotion Recognition (MER) has recently received considerable attention. To support the MER research which requires large music content libraries, we present the PMEmo dataset containing emotion annotations of 794 songs as well as the simultaneous electrodermal activity (EDA) signals. A Music Emotion Experiment was well-designed for collecting the affective-annotated music corpus of high quality, which recruited 457 subjects. The dataset is publically available to the research community, which is foremost intended for benchmarking in music emotion retrieval and recognition. To straightforwardly evaluate the methodologies for music affective analysis, it also involves pre-computed audio feature sets. In addition to that, manually selected chorus excerpts (compressed in MP3) of songs are provided to facilitate the development of chorus-related research.
@dataset{zhaorui_liu_2021_5676893,
  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Wei Li and Zijin Li},
  title        = {CCMUSIC DATABASE: Music Data Sharing Platform for Computational Musicology Research},
  month        = {nov},
  year         = {2021},
  publisher    = {Zenodo},
  version      = {1.1},
  doi          = {10.5281/zenodo.5676893},
  url          = {https://doi.org/10.5281/zenodo.5676893}
}
---
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: PMEmo
size_categories:
- n<1K
---
# Dataset Card for PMEmo
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/PMEmo>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** Dataset; Music Emotion Recognition; Experiment; EDA

### Dataset Summary
Music Emotion Recognition (MER) has recently received considerable attention. To support the MER research which requires large music content libraries, we present the PMEmo dataset containing emotion annotations of 794 songs as well as the simultaneous electrodermal activity (EDA) signals.

### Supported Tasks and Leaderboards
MER, MIR, audio classification

### Languages
Chinese, English

## Dataset Structure
### Data Instances
.zip(.wav, .txt, .lrc, .csv), .csv

### Data Fields
Audio Serial, Song Metadata, Audio Demo, Pre-computed Audio Features for Use in MER Tasks, Manually Annotated Emotion Labels, EDA Physiological Signals, Song Lyrics (LRC), Song Comments

### Data Splits
train, valid, test

## Dataset Creation
### Curation Rationale
Lack of a dataset for time-based MER

### Source Data
#### Initial Data Collection and Normalization
Kejun Zhang, Hui Zhang, Simeng Li, Changyuan Yang, Lingyun Sun, Monan Zhou

#### Who are the source language producers?
Teachers & students from NEXT Lab

### Annotations
#### Annotation process
A Music Emotion Experiment was well-designed for collecting the affective-annotated music corpus of high quality, which recruited 457 subjects. The dataset is publically available to the research community, which is foremost intended for benchmarking in music emotion retrieval and recognition. To straightforwardly evaluate the methodologies for music affective analysis, it also involves pre-computed audio feature sets. In addition to that, manually selected chorus excerpts (compressed in MP3) of songs are provided to facilitate the development of chorus-related research.

#### Who are the annotators?
Teachers & students from NEXT Lab

### Personal and Sensitive Information
None

## Considerations for Using the Data
### Social Impact of Dataset
Advancing the Digitization Process of time-based MER

### Discussion of Biases
Only for pop music

### Other Known Limitations
Time-based MER has high noise

## Additional Information
### Dataset Curators
Kejun Zhang, Hui Zhang, Simeng Li, Changyuan Yang, Lingyun Sun

### Evaluation
[Kejun Zhang, Hui Zhang, Simeng Li, Changyuan Yang, and Lingyun Sun. 2018. The PMEmo Dataset for Music Emotion Recognition. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval (ICMR '18). Association for Computing Machinery, New York, NY, USA, 135–142. https://doi.org/10.1145/3206025.3206037](https://doi.org/10.1145/3206025.3206037)

### Licensing Information
```
MIT License

Copyright (c) NEXT Lab

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```

### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu and Zijin Li},
  title        = {CCMUSIC DATASET: Music Data Sharing Platform for Computational Musicology Research},
  month        = {nov},
  year         = {2021},
  publisher    = {Zenodo},
  version      = {1.1},
  doi          = {10.5281/zenodo.5676893},
  url          = {https://doi.org/10.5281/zenodo.5676893}
}
```

### Contributions
Provide a dataset for time-based MER