---
license: cc-by-nc-4.0
task_categories:
- feature-extraction
tags:
- music
size_categories:
- 1K<n<10K
viewer: false
---

# 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},
}
```