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We study the problem of source separation for music using deep learning with four known sources: drums, bass, vocals and other accompaniments.
#3 best model for Music Source Separation on MUSDB18
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.
#5 best model for Music Source Separation on MUSDB18
Music source separation is the task of decomposing music into its constitutive components, e. g., yielding separated stems for the vocals, bass, and drums.
#2 best model for Music Source Separation on MUSDB18
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.
#4 best model for Music Source Separation on MUSDB18
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.
SOTA for Music Source Separation on MUSDB18
Based on this idea, we drive the separator towards outputs deemed as realistic by discriminator networks that are trained to tell apart real from separator samples.
This paper deals with the problem of audio source separation.
We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music.
Non-negative matrix factorization (NMF) approximates a given matrix as a product of two non-negative matrices.