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---
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:
```bibtex
@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}
} |