Music Source Separation
53 papers with code • 3 benchmarks • 7 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Music Source Separation
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Libraries
Use these libraries to find Music Source Separation models and implementations
MUSDB18
MedleyDB
Slakh2100
MIR-1K
MUSDB18-HQ
CocoChorales
MuseScore
Latest papers with no code
Hybrid Y-Net Architecture for Singing Voice Separation
This research paper presents a novel deep learning-based neural network architecture, named Y-Net, for achieving music source separation.
Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training
Jointist consists of an instrument recognition module that conditions the other two modules: a transcription module that outputs instrument-specific piano rolls, and a source separation module that utilizes instrument information and transcription results.
Multi-scale temporal-frequency attention for music source separation
In recent years, deep neural networks (DNNs) based approaches have achieved the start-of-the-art performance for music source separation (MSS).
Music Separation Enhancement with Generative Modeling
Despite phenomenal progress in recent years, state-of-the-art music separation systems produce source estimates with significant perceptual shortcomings, such as adding extraneous noise or removing harmonics.
Hierarchic Temporal Convolutional Network With Cross-Domain Encoder for Music Source Separation
In this paper, we propose a model which combines the complexed spectrogram domain feature and time-domain feature by a cross-domain encoder (CDE) and adopts the hierarchic temporal convolutional network (HTCN) for multiple music sources separation.
Feature-informed Latent Space Regularization for Music Source Separation
The integration of additional side information to improve music source separation has been investigated numerous times, e. g., by adding features to the input or by adding learning targets in a multi-task learning scenario.
On loss functions and evaluation metrics for music source separation
We investigate which loss functions provide better separations via benchmarking an extensive set of those for music source separation.
SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation
With the recent advancements of data driven approaches using deep neural networks, music source separation has been formulated as an instrument-specific supervised problem.
Distortion Audio Effects: Learning How to Recover the Clean Signal
Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing system.
Upsampling layers for music source separation
Upsampling artifacts are caused by problematic upsampling layers and due to spectral replicas that emerge while upsampling.
