Update README.md

README.md

@@ -1,3 +1,59 @@
 ---
 license: cc-by-nc-4.0
+task_categories:
+- feature-extraction
+tags:
+- music
+size_categories:
+- 1K<n<10K
 ---
+
+# CHAD-Hummings Subset
+
+This repository contains the hummings subset of the dataset from ["A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-by-Humming Task"]() (ISMIR 2023).
+
+For the complete dataset and further details, please visit the main [GitHub repository](https://github.com/amanteur/CHAD#hummings).
+
+---
+# Overview
+
+The `chad_hummings_subset.tar.gz` archive provided in this repository contains a collection of 5,314 humming audio files. 
+
+These audio files are sorted into groups of 693 distinct humming fragments originating from 311 unique songs (groups).
+
+Audio format - `.wav`.
+
+---
+
+# Dataset Structure
+
+Upon extracting the dataset from `chad_hummings_subset.tar.gz`, you will find the following structured hierarchy:
+
+```
+├── {GROUP_ID}             
+│   ├── {FRAGMENT_ID}        
+│       ├── {ID}.wav
+│       └── ...
+│   └── ...
+└── ...
+```
+where 
+- `GROUP_ID` - the unique identifier for each song,
+- `FRAGMENT_ID` - the identifier for individual fragments within a song,
+- `ID` - the version identifier for a specific fragment of the song.
+      
+This structured hierarchy organizes the audio files and fragments, making it easier to navigate and work with the dataset.
+
+---
+
+# Citation
+
+Please cite the following paper if you use the code or dataset provided in this repository.
+
+```bibtex
+@inproceedings{Amatov2023,
+    title={A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-by-Humming Task},
+    author={Amatov, Amantur and Lamanov, Dmitry and Titov, Maksim and Vovk, Ivan and Makarov, Ilya and Kudinov, Mikhail},
+    year={2023},
+}
+```