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Music source separation is the task of decomposing music into its constitutive components, e. g., yielding separated stems for the vocals, bass, and drums.

( Image credit: SigSep )

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Datasets

Greatest papers with code

Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models

ISMIR 2019 Late-Breaking/Demo 2019 deezer/spleeter

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 and speed in mind.

Ranked #8 on Music Source Separation on MUSDB18 (using extra training data)

MUSIC SOURCE SEPARATION SPEECH ENHANCEMENT

Music Source Separation in the Waveform Domain

27 Nov 2019facebookresearch/demucs

Experiments on the MusDB dataset show that Demucs beats previously reported results in terms of signal to distortion ratio (SDR), but lower than Conv-Tasnet.

AUDIO GENERATION MUSIC SOURCE SEPARATION

Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed

3 Sep 2019facebookresearch/demucs

We study the problem of source separation for music using deep learning with four known sources: drums, bass, vocals and other accompaniments.

Ranked #6 on Music Source Separation on MUSDB18 (using extra training data)

MUSIC SOURCE SEPARATION

Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation

20 Sep 2018facebookresearch/demucs

The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time-frequency representation for speech separation, and the long latency in calculating the spectrograms.

MUSIC SOURCE SEPARATION SPEAKER SEPARATION SPEECH ENHANCEMENT SPEECH SEPARATION

Open-Unmix - A Reference Implementation for Music Source Separation

The Journal of Open Source Software 2019 sigsep/open-unmix-pytorch

Music source separation is the task of decomposing music into its constitutive components, e. g., yielding separated stems for the vocals, bass, and drums.

MUSIC SOURCE SEPARATION

Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation

8 Jun 2018f90/Wave-U-Net

Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end.

AUDIO SOURCE SEPARATION MUSIC SOURCE SEPARATION

End-to-end music source separation: is it possible in the waveform domain?

29 Oct 2018francesclluis/source-separation-wavenet

Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase.

MUSIC SOURCE SEPARATION

Meta-learning Extractors for Music Source Separation

17 Feb 2020pfnet-research/meta-tasnet

We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models.

META-LEARNING MUSIC SOURCE SEPARATION

All for One and One for All: Improving Music Separation by Bridging Networks

8 Oct 2020sony/ai-research-code

This paper proposes several improvements for music separation with deep neural networks(DNNs), namely a multi-domain loss(MDL) and two combination schemes.

MUSIC SOURCE SEPARATION AUDIO AND SPEECH PROCESSING SOUND