Librispeech Slakh Unmix (LSX)

Introduction

Librispeech Slakh Unmix (LSX) is a proof of concept source separation dataset for training and testing algorithms that separate a monaural audio signal using hyperbolic embeddings for hierarchical separation. The dataset is composed of artificial mixtures using audio from the librispeech (clean subset) and Slakh2100 datasets. The dataset was introduced in our paper Hyperbolic Audio Source Separation.

At a Glance

The size of the unzipped dataset is ~28GB

Each mixture is 60-s in length and denotes the first 60 s of the bass, drums, and guitar stems of the associated Slakh2100 track.

Audio is encoded as 16 bit wav files at a sampling rate of 16 kHz

The data is split into training tr (1390 mixtues), validation cv (348 mixtures) and testing tt (209 mixtures) subsets

The directory for each mixture contains eight wav files:

mix.wav the overall mixture from the five child sources

music_mix.wav the music submix containing guitar, bass, and drums

speech_mix.wav the speech submix containing both male and female speech signals

bass.wav original bass submix from slakh track

drums.wav original drums submix from slakh track

guitar.wav original guitar submix from slakh track

speech_male.wav concatenated male speech utterances filling the length of the song

speech_female.wav concatenated female speech utterances filling the length of the song

Other Resources

Pytorch code for training models along with our hyperbolic separation interface are available here

Citation

If you use LSX in your research, please cite our paper:

@InProceedings{Petermann2023ICASSP_hyper,
author = {Petermann, Darius and Wichern, Gordon and Subramanian, Aswin and {Le Roux}, Jonathan},
title = {Hyperbolic Audio Source Separation},
booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = 2023,
month = jun
}

Copyright and License

The LSX dataset is released under CC-BY-4.0 license.

All data:

Created by Mitsubishi Electric Research Laboratories (MERL), 2022-2023

SPDX-License-Identifier: CC-BY-4.0