File size: 1,774 Bytes
2e8601a e3ad494 8b079ae 2e8601a e3ad494 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
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},
}
``` |