Spleeter: a fast and efficient music source separation tool with pre-trained models
By Romain Hennequin, Anis Khlif, Félix Voituret, Manuel Moussallam. 2020
363 citations recorded by the source corpus.
Summary We present and release a new tool for music source separation with pre-trained models called Spleeter. Spleeter was designed with ease of use, separation performance
Subfields: Computer Vision and Pattern Recognition, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Dacheng Tao
- Xiaogang Wang
- Cordelia Schmid
- Trevor Darrell
- Ming-Hsuan Yang
- Bernt Schiele
- Andrew Zisserman
In Signal Processing
- Alexandre D'efossez
- Francis R. Bach
- Francisco Massa
- Nicolas Usunier
- Simon Rouard
- L. Eon Bottou
- Yossi Adi
- Gabriel Synnaeve
Citations in the corpus
- Music Source Separation in the Waveform Domain (2019, methodology): “We also compare to the more recent options like the D3Net [Takahashi and Mitsufuji, 2020] model, which used dilated convolutions with dense connections, as well as the Spleeter model [Hennequin et al., 2020], a U-Net model trained on a very diverse dataset.”
- Hybrid Transformers for Music Source Separation (2022, methodology): “Using large datasets has been shown to be beneficial to the task of MSS. Spleeter [19] is a spectrogram masking U-Net architecture trained on 25,000 songs extracts of 30 seconds, and was at the time of its release, the best model available.”