FMAR: A Dataset for Robust Song Identification

Authors: Ryan Lee, Yi-Chieh Chiu, Abhir Karande, Ayush Goyal, Harrison Pearl, Matthew Hong, Spencer Cobb

Overview

To improve copyright infringement detection, we introduce Free-Music-Archive-Retrieval (FMAR), a structured dataset designed to test a model's capability to identify songs based on 5-second clips, or queries. We create adversarial queries to replicate common strategies to evade copyright infringement detectors, such as pitch shifting, EQ balancing, and adding background noise.

Source

This dataset is sourced from the benjamin-paine/free-music-archive-small collection on Hugging Face. It includes:

Total Audio Tracks: 7,916
Average Duration: Approximately 30 seconds per track
Diversity: Multiple genres to ensure a diverse representation of musical styles

Background noises applied to the adversarial queries were sourced from the following work:

@inproceedings{piczak2015dataset,
  title = {{ESC}: {Dataset} for {Environmental Sound Classification}},
  author = {Piczak, Karol J.},
  booktitle = {Proceedings of the 23rd {Annual ACM Conference} on {Multimedia}},
  date = {2015-10-13},
  url = {http://dl.acm.org/citation.cfm?doid=2733373.2806390},
  doi = {10.1145/2733373.2806390},
  location = {{Brisbane, Australia}},
  isbn = {978-1-4503-3459-4},
  publisher = {{ACM Press}},
  pages = {1015--1018}
}