Datasets:

mippia
/

SMP_dataset

license: mit
language:
  - en
  - ko
tags:
  - music
  - music-information-retrieval
  - plagiarism-detection
  - music-similarity
task_categories:
  - audio-classification
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: smp_dataset.csv
arxiv: 2601.2126

Similar Music Pair (SMP) Dataset

The SMP (Segment-based Music Plagiarism) dataset contains music plagiarism detection pairs with temporal segment annotations. Each row represents a pair of songs with identified similar segments.

This dataset accompanies the paper Music Plagiarism Detection: Problem Formulation and a Segment-based Solution.

Code: https://github.com/Mippia/Music-Plagiarism-Detection
Demo: https://huggingface.co/spaces/mippia/MPD-demo
Project page: https://mippia.github.io/icassp-mpd/

Dataset Structure

Column	Description
`ori_title`	Title of the original song
`comp_title`	Title of the comparison song
`ori_link`	YouTube link to the original song
`comp_link`	YouTube link to the comparison song
`relation`	Relationship type (e.g., `plag` for plagiarism)
`ori_times`	List of start times (seconds) of similar segments in the original song
`comp_times`	List of start times (seconds) of similar segments in the comparison song
`pair_number`	Unique identifier for song pairs
`acoustic_idx`	Unique identifier for segment pairs

Time annotations are JSON-formatted lists; ori_times[i] and comp_times[i] correspond to the same matching segment between the two songs.

Usage

from datasets import load_dataset

dataset = load_dataset("mippia/SMP-dataset")
print(dataset["train"][0])

Notes

Audio is not included. Only YouTube links and temporal annotations are provided. Users must retrieve audio from YouTube themselves.
The dataset includes both English and Korean songs across multiple genres.

Citation

@inproceedings{go2026mpd,
  title={Music Plagiarism Detection: Problem Formulation and a Segment-based Solution},
  author={Go, Seonghyeon and Kim, Yumin},
  booktitle={ICASSP},
  year={2026}
}

License

Released under MIT License.