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
Why does music source separation benefit from cacophony?
In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs.
Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet
There have been significant advances in deep learning for music demixing in recent years.
SCNet: Sparse Compression Network for Music Source Separation
We use a higher compression ratio on subbands with less information to improve the information density and focus on modeling subbands with more information.
Resource-constrained stereo singing voice cancellation
We study the problem of stereo singing voice cancellation, a subtask of music source separation, whose goal is to estimate an instrumental background from a stereo mix.
Subnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference
Deploying neural networks to different devices or platforms is in general challenging, especially when the model size is large or model complexity is high.
Pre-trained Spatial Priors on Multichannel NMF for Music Source Separation
This paper presents a novel approach to sound source separation that leverages spatial information obtained during the recording setup.
MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement
A typical neural speech enhancement (SE) approach mainly handles speech and noise mixtures, which is not optimal for singing voice enhancement scenarios.
Contrastive Learning based Deep Latent Masking for Music Source Separation
Recent studies on music source separation have extended their applicability to generic audio signals.
Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data
Music source separation (MSS) faces challenges due to the limited availability of correctly-labeled individual instrument tracks.
Pac-HuBERT: Self-Supervised Music Source Separation via Primitive Auditory Clustering and Hidden-Unit BERT
In this paper, we propose a self-supervised learning framework for music source separation inspired by the HuBERT speech representation model.
