README.md

---
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}
}