Update README.md

README.md

@@ -1 +1,42 @@
-To improve copyright infringement detection, we developed a structured dataset designed to test a model's capability to identify songs based on 5 second clips. We extract a random 5 second span from the original song audio as query. We then define adversarial queries, such as adding background noise, pitch shifting and EQ balancing, to fool detectors. The dataset is sourced from the \texttt{benjamin-paine/free-music-archive-small} collection on Hugging Face, containing a total of 7,916 audio tracks. Each track has an approximate duration of 30 seconds and spans multiple genres, ensuring a diverse representation of musical styles. This dataset provides a comprehensive foundation for retrieval-based copyright detection, offering varied audio patterns necessary for robust model training and evaluation.
+# FMAR: A Dataset for Robust Song Identification
+
+**Authors:** Ryan Lee, Yi-Chieh Chiu, Ayush Goyal, Abhir Karande, Harrison Pearl, Matthew Hong, Spencer Cobb
+
+## Overview
+
+To improve copyright infringement detection, we developed 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}
+}