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169
Musical Source Separation using Generalised NonNegative Tensor Factorisation models
"... A shiftinvariant nonnegative tensor factorisation algorithm for musical source separation is proposed which generalises previous work by allowing each source to have its own parameters rather a fixed set of parameters for all sources. This allows independent control of the number of allowable note ..."
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Cited by 2 (2 self)
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A shiftinvariant nonnegative tensor factorisation algorithm for musical source separation is proposed which generalises previous work by allowing each source to have its own parameters rather a fixed set of parameters for all sources. This allows independent control of the number of allowable
Extended Nonnegative Tensor Factorisation models for Musical Sound Source Separation
"... Recently, shift invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, existing algorithms require the use of logfrequency spectrograms to allow shift invariance in frequency which causes problems when attemp ..."
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Recently, shift invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, existing algorithms require the use of logfrequency spectrograms to allow shift invariance in frequency which causes problems when
USING TENSOR FACTORISATION MODELS TO SEPARATE DRUMS FROM POLYPHONIC MUSIC
"... This paper describes the use of Nonnegative Tensor Factorisation models for the separation of drums from polyphonic audio. Improved separation of the drums is achieved through the incorporation of Gamma Chain priors into the Nonnegative Tensor Factorisation framework. In contrast to many previo ..."
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Cited by 5 (1 self)
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This paper describes the use of Nonnegative Tensor Factorisation models for the separation of drums from polyphonic audio. Improved separation of the drums is achieved through the incorporation of Gamma Chain priors into the Nonnegative Tensor Factorisation framework. In contrast to many
Shifted 2D Nonnegative Tensor Factorisation
"... ... developed as a means of separating harmonic instruments from single channel mixtures. This technique uses a model which is convolutive in both time and frequency, and so can capture instruments which have both timevarying spectra and timevarying fundamental frequencies simultaneously. However, ..."
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Cited by 5 (2 self)
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, in many cases two or more channels are available, in which case it would be advantageous to have a multichannel version of the algorithm. To this end, a shifted 2D Nonnegative Tensor Factorisation algorithm is derived, which extends Nonnegative Matrix Factor 2D Deconvolution to the multichannel case
Nonnegative tensor factorisation for sound source separation
 IN: PROCEEDINGS OF IRISH SIGNALS AND SYSTEMS CONFERENCE
, 2005
"... ... is introduced which extends current matrix factorisation techniques to deal with tensors. The effectiveness of the algorithm is then demonstrated through tests on synthetic data. The algorithm is then employed as a means of performing sound source separation on two channel mixtures, and the sepa ..."
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Cited by 28 (2 self)
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... is introduced which extends current matrix factorisation techniques to deal with tensors. The effectiveness of the algorithm is then demonstrated through tests on synthetic data. The algorithm is then employed as a means of performing sound source separation on two channel mixtures
Generalised Coupled Tensor Factorisation
"... We derive algorithms for generalised tensor factorisation (GTF) by building upon the wellestablished theory of Generalised Linear Models. Our algorithms are general in the sense that we can compute arbitrary factorisations in a message passing framework, derived for a broad class of exponential fam ..."
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We derive algorithms for generalised tensor factorisation (GTF) by building upon the wellestablished theory of Generalised Linear Models. Our algorithms are general in the sense that we can compute arbitrary factorisations in a message passing framework, derived for a broad class of exponential
SemiSupervised NonNegative Tensor Factorisation of Modulation Spectrograms for Monaural Speech Separation
"... Abstract—This paper details the use of a semisupervised approach to audio source separation. Where only a single source model is available, the model for an unknown source must be estimated. A mixture signal is separated through factorisation of a featuretensor representation, based on the modulat ..."
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, the proposed semisupervised method outperformed both semisupervised nonnegative matrix factorisation and blind nonnegative modulation spectrum tensor factorisation. I.
Sound Source Separation using Shifted Nonnegative Tensor Factorisation
 Proceedings on the IEE Conference on Audio and Speech Signal Processing (ICASSP
, 2006
"... Recently, shifted Nonnegative Matrix Factorisation was developed as a means of separating harmonic instruments from single channel mixtures. However, in many cases two or more channels are available, in which case it would be advantageous to have a multichannel version of the algorithm. To this end ..."
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Cited by 15 (0 self)
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. To this end, a shifted Nonnegative Tensor Factorisation algorithm is derived, which extends shifted Nonnegative Matrix Factorisation to the multichannel case. The use of this algorithm for multichannel sound source separation of harmonic instruments is demonstrated. Further, it is shown that the algorithm
Nonnegative tensor factorization with applications to statistics and computer vision
 In Proceedings of the International Conference on Machine Learning (ICML
, 2005
"... We derive algorithms for finding a nonnegative ndimensional tensor factorization (nNTF) which includes the nonnegative matrix factorization (NMF) as a particular case when n = 2. We motivate the use of nNTF in three areas of data analysis: (i) connection to latent class models in statistics, (ii ..."
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Cited by 139 (5 self)
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We derive algorithms for finding a nonnegative ndimensional tensor factorization (nNTF) which includes the nonnegative matrix factorization (NMF) as a particular case when n = 2. We motivate the use of nNTF in three areas of data analysis: (i) connection to latent class models in statistics
Bayesian extensions to nonnegative matrix factorisation for audio signal modelling
 in ICASSP, 2008
"... We describe the underlying probabilistic generative signal model of nonnegative matrix factorisation (NMF) and propose a realistic conjugate priors on the matrices to be estimated. A conjugate Gamma chain prior enables modelling the spectral smoothness of natural sounds in general, and other prior ..."
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Cited by 42 (6 self)
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We describe the underlying probabilistic generative signal model of nonnegative matrix factorisation (NMF) and propose a realistic conjugate priors on the matrices to be estimated. A conjugate Gamma chain prior enables modelling the spectral smoothness of natural sounds in general, and other prior
Results 1  10
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169