language	pretty_name	tags	license	license_name	license_link
en	MoisesDB	audio music source separation	other	ncrlc	https://github.com/moises-ai/moises-db

MoisesDB

Moises Dataset for Source Separation

Dataset Description

• Homepage: MoisesDB homepage
• Repository: MoisesDB repository
• Paper: Moisesdb: A dataset for source separation beyond 4-stems
• Point of Contact: Igor Pereira

Dataset Summary

MoisesDB is a dataset for source separation. It provides a collection of tracks and their separated stems (vocals, bass, drums, etc.). The dataset is used to evaluate the performance of source separation algorithms.

Download the data

Please download the dataset at our research website, extract it and configure the environment variable MOISESDB_PATH accordingly.

export MOISESDB_PATH=./moises-db-data

The directory structure should be

moisesdb:
    moisesdb_v0.1
        track uuid 0
        track uuid 1
        .
        .
        .

Install

You can install this package with

pip install git+https://github.com/moises-ai/moises-db.git

Usage

MoisesDB

After downloading and configuring the path for the dataset, you can create an instance of MoisesDB to access the tracks. You can also provide the dataset path with the data_path argument.

from moisesdb.dataset import MoisesDB

db = MoisesDB(
    data_path='./moisesdb',
    sample_rate=44100
)

The MoisesDB object has iterator properties that you can use to access all files within the dataset.

n_songs = len(db)
track = db[0]  # Returns a MoisesDBTrack object

MoisesDBTrack

The MoisesDBTrack object holds information about a track in the dataset, perform on-the-fly mixing for stems and multiple sources within a stem.

You can access all the stems and mixture from the stem and audio properties. The stem property returns a dictionary whith available stems as keys and nd.array on values. The audio property results in a nd.array with the mixture.

track = db[0]
stems = track.stems  # stems = {'vocals': ..., 'bass': ..., ...}
mixture track.audio # mixture = nd.array

The MoisesDBTrack object also contains other non-audio information from the track such as:

• track.id
• track.provider
• track.artist
• track.name
• track.genre
• track.sources
• track.bleedings
• track.activity

The stems and mixture are computed on-the-fly. You can create a stems-only version of the dataset using the save_stems method of the MoisesDBTrack.

track = db[0]
path =  './moises-db-stems/0'
track.save_stems(path)

Performance Evaluation

We run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the MoisesDB. These results are located in csv files at the benchmark folder.

Citing

If you used the MoisesDB dataset on your research, please cite the following paper.

@misc{pereira2023moisesdb,
      title={Moisesdb: A dataset for source separation beyond 4-stems}, 
      author={Igor Pereira and Felipe Araújo and Filip Korzeniowski and Richard Vogl},
      year={2023},
      eprint={2307.15913},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

Licensing

MoisesDB is distributed with the NC-RCL license.

"Non-Commercial Research Community license (NC-RCL)

Limited Redistribution: You are permitted to copy and utilize the provided audio material in any medium or format, as long as it is done only for non-commercial purposes within the research community, and the redistribution is conducted solely through the platform moises.ai or other platforms explicitly authorized by the licensor. Redistribution outside the authorized platforms is not allowed without the licensor's written consent.

Attribution: You must give appropriate credit (including the artist's name and the song's title), and provide a link to this license or a notice indicating the terms of this license.

Non-Commercial Use: You cannot use the material for any commercial purposes or financial gain. This includes, but is not limited to, the sale, licensing, or rental of the material, as well as any use where the primary aim is to generate revenue or profits.

No Derivative Works: You cannot create, remix, adapt, or build upon the material, unless explicitly permitted by the artist.

Preservation of Legal Notices: You cannot remove any copyright or other proprietary notices which are included in or attached to the material.

Termination: If you fail to comply with this license, your rights to use the material will be terminated automatically.

Voice Cloning Restriction: You are prohibited from using the vocal stems or any part of the audio material to create a public digital imitation of the artist's voice (e.g: a vocal clone or replica). This includes, but is not limited to, the utilization of voice synthesis technology, deep learning algorithms, and other artificial intelligence-based tools."