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Performance measurement in blind audio source separation
TLDR
This paper considers four different sets of allowed distortions in blind audio source separation algorithms, from time-invariant gains to time-varying filters, and derives a global performance measure using an energy ratio, plus a separate performance measure for each error term.
View on IEEE
Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis
TLDR
Results indicate that IS-NMF correctly captures the semantics of audio and is better suited to the representation of music signals than NMF with the usual Euclidean and KL costs.
View on MIT Press
Algorithms for Nonnegative Matrix Factorization with the β-Divergence
This letter describes algorithms for nonnegative matrix factorization (NMF) with the β-divergence (β-NMF). The β-divergence is a family of cost functions parameterized by a single shape parameter β…
View on MIT Press
Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation
  • A. OzerovC. Févotte
  • Computer Science
    IEEE Transactions on Audio, Speech, and Language…
  • 1 March 2010
We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals. We work in the…
View on IEEE
BSS_EVAL Toolbox User Guide -- Revision 2.0
TLDR
The purpose of this toolbox is to measure the performance of various source separation algorithms in an evaluation framework where the original sources, and perhaps even the noise that perturbed the mixture, are available for comparison.
Source/Filter Model for Unsupervised Main Melody Extraction From Polyphonic Audio Signals
TLDR
A new signal model is proposed where the leading vocal part is explicitly represented by a specific source/filter model and reaches state-of-the-art performances on all test sets.
View on IEEE
Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization
TLDR
A robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures is introduced, which leads to a new form of robust nonnegative matrix factorization with a group-sparse outlier term.
View on IEEE
Automatic Relevance Determination in Nonnegative Matrix Factorization with the-Divergence
TLDR
A Bayesian model based on automatic relevance determination (ARD) in which the columns of the dictionary matrix and the rows of the activation matrix are tied together through a common scale parameter in their prior is proposed.
Algorithms for nonnegative matrix factorization with the beta-divergence
This paper describes algorithms for nonnegative matrix factorization (NMF) with the beta-divergence (beta-NMF). The beta-divergence is a family of cost functions parametrized by a single shape…
View PDF on arXiv
Proposals for Performance Measurement in Source Separation
TLDR
When the sources are estimated from a degenerate set of mixtures by applying a demixing matrix, it is proved that there are upper bounds on the achievable Source to Interference Ratio.
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