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--- |
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dataset_info: |
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features: |
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- name: audio |
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dtype: audio |
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- name: amusing |
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dtype: float64 |
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- name: angry |
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dtype: float64 |
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- name: annoying |
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dtype: float64 |
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- name: anxious/tense |
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dtype: float64 |
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- name: awe-inspiring/amazing |
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dtype: float64 |
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- name: beautiful |
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dtype: float64 |
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- name: bittersweet |
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dtype: float64 |
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- name: calm/relaxing/serene |
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dtype: float64 |
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- name: compassionate/sympathetic |
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dtype: float64 |
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- name: dreamy |
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dtype: float64 |
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- name: eerie/mysterious |
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dtype: float64 |
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- name: energizing/pump-up |
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dtype: float64 |
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- name: entrancing |
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dtype: float64 |
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- name: erotic/desirous |
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dtype: float64 |
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- name: euphoric/ecstatic |
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dtype: float64 |
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- name: exciting |
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dtype: float64 |
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- name: goose bumps |
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dtype: float64 |
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- name: indignant/defiant |
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dtype: float64 |
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- name: joyful/cheerful |
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dtype: float64 |
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- name: nauseating/revolting |
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dtype: float64 |
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- name: painful |
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dtype: float64 |
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- name: proud/strong |
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dtype: float64 |
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- name: romantic/loving |
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dtype: float64 |
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- name: sad/depressing |
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dtype: float64 |
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- name: scary/fearful |
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dtype: float64 |
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- name: tender/longing |
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dtype: float64 |
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- name: transcendent/mystical |
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dtype: float64 |
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- name: triumphant/heroic |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 166110026.787 |
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num_examples: 1841 |
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download_size: 159674012 |
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dataset_size: 166110026.787 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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task_categories: |
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- feature-extraction |
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- audio-classification |
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tags: |
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- Music |
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- Emotion |
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- Recognition |
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- MERT |
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- Dataset |
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- Audio |
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pretty_name: 13 Dimension Emotions Dataset |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card for **Music Emotion Ratings Across Cultures** |
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This dataset captures the mean emotional category ratings for 1,841 music samples based on subjective experiences reported by participants from the United States and China. The ratings were collected as part of the study to uncover the **universal and nuanced emotions** evoked by instrumental music. |
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--- |
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## Dataset Details |
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### Dataset Sources |
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- **Paper**: [What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures](https://www.pnas.org/cgi/doi/10.1073/pnas.1910704117) |
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- **Demo (Interactive Map)**: [Music Emotion Map](https://www.ocf.berkeley.edu/~acowen/music.html) |
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--- |
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## Uses |
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### Direct Use |
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This dataset is designed for: |
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- **Music Emotion Classification**: Training multi-label classifiers for identifying emotions in music based on 13 universal categories. |
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- **Cross-Cultural Emotion Analysis**: Analyzing similarities and differences in emotional responses to music across cultures. |
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- **Emotion Visualization**: Creating high-dimensional visualizations of emotional distributions in music. |
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### Out-of-Scope Use |
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The dataset is **not suitable** for: |
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- Identifying lyrics-related emotions (as the music is instrumental). |
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- Cultural or genre-specific emotional predictions outside the U.S. and China. |
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- Misuse for building biased systems that assume emotional responses are fixed across all populations. |
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--- |
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## Dataset Structure |
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### Data Fields |
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- **Sample ID**: Unique identifier for each of the 2,168 music clips. |
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- **Category Ratings**: Mean ratings for each of the 13 universal emotional categories: |
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- Joyful/Cheerful |
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- Calm/Relaxing |
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- Sad/Depressing |
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- Scary/Fearful |
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- Triumphant/Heroic |
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- Energizing/Pump-up |
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- Dreamy |
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- Romantic/Loving |
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- Amusing |
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- Exciting |
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- Compassionate/Sympathetic |
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- Awe-Inspiring |
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- Eerie/Mysterious |
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- **Valence**: Mean ratings for pleasantness (positive or negative feelings). |
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- **Arousal**: Mean ratings for energy levels (calm or excited feelings). |
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### Splits |
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The dataset does not use predefined splits but can be segmented based on: |
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- **Cultural groups**: U.S. vs. China. |
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- **Emotional dimensions**: Individual emotional categories or broad features like valence/arousal. |
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--- |
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## Dataset Creation |
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### Curation Rationale |
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The dataset was created to: |
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- **Map Universal Emotions in Music**: Investigate whether emotional experiences evoked by music are universal across cultures. |
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- **Broaden Emotional Taxonomies**: Move beyond traditional models that use only 6 emotions or simple valence/arousal dimensions. |
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- **Enable Nuanced Emotional Understanding**: Provide a high-dimensional framework for understanding and classifying emotional responses to music. |
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--- |
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### Source Data |
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- **Original Sources**: Instrumental music samples (5 seconds each) were contributed by participants to represent specific emotional categories. |
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- **Annotations**: Ratings collected through large-scale crowdsourcing from 1,591 U.S. and 1,258 Chinese participants. |
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--- |
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## License |
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[More Information Needed] |
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--- |
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## Citation |
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If you use this dataset, please cite the following paper: |
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Cowen, A. S., Fang, X., Sauter, D., & Keltner, D. (2020). What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures. PNAS, 117(4), 1924-1934. https://doi.org/10.1073/pnas.1910704117 |
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### Source Data |
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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#### Data Collection and Processing |
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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[More Information Needed] |
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#### Who are the source data producers? |
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<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> |
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[More Information Needed] |
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### Annotations [optional] |
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> |
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#### Annotation process |
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> |
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[More Information Needed] |
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#### Who are the annotators? |
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<!-- This section describes the people or systems who created the annotations. --> |
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[More Information Needed] |
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#### Personal and Sensitive Information |
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<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Dataset Card Authors [optional] |
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[More Information Needed] |
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## Dataset Card Contact |
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[More Information Needed] |
