ml-ryanlee/free-music-archive-retrieval

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.

Dataset Description

• —Query Audio: A random 5-second span is extracted from the original song audio.

• —Adversarial Queries: We define adversarial queries by applying modifications such as:
• —Adding background noise
• —Pitch shifting
• —EQ balancing

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}
}