Multichannel Blind Music Source Separation Using Directivity-Aware MNMF With Harmonicity Constraints

In this paper we present a harmonic constrained Multichannel Non-Negative Matrix Factorization (MNMF) method for the task of blind music source separation. In this model, the mixing filter encodes the spatial information in terms of magnitude and phase differences between channels whereas the source...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.17781-17795
Main Authors: Munoz-Montoro, Antonio J., Carabias-Orti, Julio J., Cabanas-Molero, Pablo, Canadas-Quesada, Francisco J., Ruiz-Reyes, Nicolas
Format: Article
Language:English
Subjects:
Algorithms
Computational modeling
Constant-Q transform
Constraints
Covariance matrix
Directivity
Harmonic analysis
harmonicity
Kernel
Mathematical models
multichannel NMF
Music
music source separation
Parameter sensitivity
Separation
Source separation
Spatial data
Spectrogram
Time-frequency analysis
Transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we present a harmonic constrained Multichannel Non-Negative Matrix Factorization (MNMF) method for the task of blind music source separation. In this model, the mixing filter encodes the spatial information in terms of magnitude and phase differences between channels whereas the source variances are modelled using a harmonic constrained NMF structure. In this work, the spatial covariance matrix is obtained from the constant-Q transform to account for the frequency logarithmic scale inherent in music signals and reduce the dimensionality of the parameters. Moreover, to mitigate the strong sensitivity to parameter initialization, we propose to initialize the spatial weights with the output of the steered response power (SRP) with the phase transform (PHAT) algorithm. The proposed method has been evaluated for the task of music source separation using a multichannel classical chamber music dataset with several polyphony and reverberation setups. Furthermore, a comparison with other state-of-the-art signal decomposition methods has been accomplished showing reliable results in terms of BSS_EVAL metrics.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3150248