Update README.md

README.md

@@ -1,13 +1,25 @@
-# FMAR: A Dataset for Robust Song Identification
+---
+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.
   
@@ -18,15 +30,12 @@ To improve copyright infringement detection, we introduce Free-Music-Archive-Ret
   - 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}},
@@ -39,4 +48,4 @@ Background noises applied to the adversarial queries were sourced from the follo
   isbn = {978-1-4503-3459-4},
   publisher = {{ACM Press}},
   pages = {1015--1018}
-}
+}