language:
- en
pretty_name: Free Music Archive Retrieval
tags:
- audio
- english
- music
- retrieval
license: mit
task_categories:
- audio-classification
- audio-to-audio
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}
}