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Da-TACOS

We present Da-TACOS: a dataset for cover song identification and understanding. It contains two subsets, namely the benchmark subset (for benchmarking cover song identification systems) and the cover analysis subset (for analyzing the links among cover songs), with pre-extracted features and metadata for 15,000 and 10,000 songs, respectively. The annotations included in the metadata are obtained with the API of SecondHandSongs.com. All audio files we use to extract features are encoded in MP3 format and their sample rate is 44.1 kHz. Da-TACOS does not contain any audio files. For the results of our analyses on modifiable musical characteristics using the cover analysis subset and our initial benchmarking of 7 state-of-the-art cover song identification algorithms on the benchmark subset, you can look at our publication.

For organizing the data, we use the structure of SecondHandSongs where each song is called a 'performance', and each clique (cover group) is called a 'work'. Based on this, the file names of the songs are their unique performance IDs (PID, e.g. P_22), and their labels with respect to their cliques are their work IDs (WID, e.g. W_14).

Metadata for each song includes

  • performance title,
  • performance artist,
  • work title,
  • work artist,
  • release year,
  • SecondHandSongs.com performance ID,
  • SecondHandSongs.com work ID,
  • whether the song is instrumental or not.

In addition, we matched the original metadata with MusicBrainz to obtain MusicBrainz ID (MBID), song length and genre/style tags. We would like to note that MusicBrainz related information is not available for all the songs in Da-TACOS, and since we used just our metadata for matching, we include all possible MBIDs for a particular songs.

For facilitating reproducibility in cover song identification (CSI) research, we propose a framework for feature extraction and benchmarking in our supplementary repository: acoss. The feature extraction component is designed to help CSI researchers to find the most commonly used features for CSI in a single address. The parameter values we used to extract the features in Da-TACOS are shared in the same repository. Moreover, the benchmarking component includes our implementations of 7 state-of-the-art CSI systems. We provide the performance results of an initial benchmarking of those 7 systems on the benchmark subset of Da-TACOS. We encourage other CSI researchers to contribute to acoss with implementing their favorite feature extraction algorithms and their CSI systems to build up a knowledge base where CSI research can reach larger audiences.

The instructions for how to download and use the dataset are shared below. Please contact us if you have any questions or requests.

Structure

Metadata

We provide two metadata files that contain information about the benchmark subset and the cover analysis subset. Both metadata files are stored as python dictionaries in .json format, and have the same hierarchical structure.

An example to load the metadata files in python:

import json

with open('./da-tacos_metadata/da-tacos_benchmark_subset_metadata.json') as f:
	benchmark_metadata = json.load(f)

The python dictionary obtained with the code above will have the respective WIDs as keys. Each key will provide the song dictionaries that contain the metadata regarding the songs that belong to their WIDs. An example can be seen below:

"W_163992": { # work id
	"P_547131": { # performance id of the first song belonging to the clique 'W_163992'
		"work_title": "Trade Winds, Trade Winds",
		"work_artist": "Aki Aleong",
		"perf_title": "Trade Winds, Trade Winds",
		"perf_artist": "Aki Aleong",
		"release_year": "1961",
		"work_id": "W_163992",
		"perf_id": "P_547131",
		"instrumental": "No",
		"perf_artist_mbid": "9bfa011f-8331-4c9a-b49b-d05bc7916605",
		"mb_performances": {
			"4ce274b3-0979-4b39-b8a3-5ae1de388c4a": {
				"length": "175000"
			},
			"7c10ba3b-6f1d-41ab-8b20-14b2567d384a": {
				"length": "177653"
			}
		}
	},
	"P_547140": { # performance id of the second song belonging to the clique 'W_163992'
		"work_title": "Trade Winds, Trade Winds",
		"work_artist": "Aki Aleong",
		"perf_title": "Trade Winds, Trade Winds",
		"perf_artist": "Dodie Stevens",
		"release_year": "1961",
		"work_id": "W_163992",
		"perf_id": "P_547140",
		"instrumental": "No"
	}
}

Pre-extracted features

The list of features included in Da-TACOS can be seen below. All the features are extracted with acoss repository that uses open-source feature extraction libraries such as Essentia, LibROSA, and Madmom.

To facilitate the use of the dataset, we provide two options regarding the file structure.

1- In da-tacos_benchmark_subset_single_files and da-tacos_coveranalysis_subset_single_files folders, we organize the data based on their respective cliques, and one file contains all the features for that particular song.

{
	"chroma_cens": numpy.ndarray,
	"crema": numpy.ndarray,
	"hpcp": numpy.ndarray,
	"key_extractor": {
		"key": numpy.str_,
		"scale": numpy.str_,_
		"strength": numpy.float64
	},
	"madmom_features": {
		"novfn": numpy.ndarray, 
		"onsets": numpy.ndarray,
		"snovfn": numpy.ndarray,
		"tempos": numpy.ndarray
	}
	"mfcc_htk": numpy.ndarray,
	"tags": list of (numpy.str_, numpy.str_)
	"label": numpy.str_,
	"track_id": numpy.str_
}

2- In da-tacos_benchmark_subset_FEATURE and da-tacos_coveranalysis_subset_FEATURE folders, the data is organized based on their cliques as well, but each of these folders contain only one feature per song. For instance, if you want to test your system that uses HPCP features, you can download da-tacos_benchmark_subset_hpcp to access the pre-computed HPCP features. An example for the contents in those files can be seen below:

{
	"hpcp": numpy.ndarray,
	"label": numpy.str_,
	"track_id": numpy.str_
}

Using the dataset

Requirements

  • Python 3.6+
  • Create virtual environment and install requirements
git clone https://github.com/MTG/da-tacos.git
cd da-tacos
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Downloading the data

The dataset is currently stored in only in Google Drive (it will be uploaded to Zenodo soon), and can be downloaded from this link. We also provide a python script that automatically downloads the folders you specify. Basic usage of this script can be seen below:

python download_da-tacos.py -h
usage: download_da-tacos.py [-h]
                            [--dataset {benchmark,coveranalysis,da-tacos}]                                                                          
                            [--type {single_files,cens,crema,hpcp,key,madmom,mfcc,tags}]   
                            [--source {gdrive,zenodo}]                                                         
                            [--outputdir OUTPUTDIR]
                            [--unpack]
                            [--remove]

Download script for Da-TACOS 

optional arguments:                                                                                                       
  -h, --help            show this help message and exit                                                                   
  --dataset {metadata,benchmark,coveranalysis,da-tacos}                                                                      
                        which subset to download. 'da-tacos' option downloads
                        both subsets. the options other than 'metadata' will
                        download the metadata as well. (default: metadata)                                                                     
  --type {single_files,cens,crema,hpcp,key,madmom,mfcc,tags} [{single_files,cens,crema,hpcp,key,madmom,mfcc,tags} ...]                                     
                        which folder to download. for downloading multiple
                        folders, you can enter multiple arguments (e.g. '--
                        type cens crema'). for detailed explanation, please
                        check https://mtg.github.io/da-tacos/ (default:
                        single_files)                  
  --source {gdrive,zenodo}
                        from which source to download the files. you can
                        either download from Google Drive (gdrive) or from
                        Zenodo (zenodo) (default: gdrive)                                           
  --outputdir OUTPUTDIR                                                               
                        directory to store the dataset (default: ./)                   
  --unpack              unpack the zip files (default: False)                        
  --remove              remove zip files after unpacking (default: False) 

Loading the data in python

All files (except the metadata) are stored in .h5 format. We recommend using deepdish library for python to load the files. An example of how to load the data is shown below:

import deepdish as dd

file_path = './da-tacos_coveranalysis_subset_single_files/W_14/P_15.h5'
P_15_data = dd.io.load(file_path)

Citing the dataset

Please cite the following publication when using the dataset:

Furkan Yesiler, Chris Tralie, Albin Correya, Diego F. Silva, Philip Tovstogan, Emilia Gómez, and Xavier Serra. Da-TACOS: A Dataset for Cover Song Identification and Understanding. In Proc. of the 20th Int. Soc. for Music Information Retrieval Conf. (ISMIR), pages 327-334, Delft, The Netherlands, 2019.

Bibtex version:

@inproceedings{yesiler2019,
    author = "Furkan Yesiler and Chris Tralie and Albin Correya and Diego F. Silva and Philip Tovstogan and Emilia G{\'{o}}mez and Xavier Serra",
    title = "{Da-TACOS}: A Dataset for Cover Song Identification and Understanding",
    booktitle = "Proc. of the 20th Int. Soc. for Music Information Retrieval Conf. (ISMIR)",
    year = "2019",
    pages = "327--334",
    address = "Delft, The Netherlands"
}

License

  • The code in this repository is licensed under Apache 2.0
  • The metadata and the pre-extracted features are licensed under CC BY-NC-SA 4.0

Copyright 2019 Music Technology Group

Acknowledgments

This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068 (MIP-Frontiers).

This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 770376 (TROMPA).

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