Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
393
Shifted 2D Non-negative 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 time-varying spectra and timevarying fundamental frequencies simultaneously. However, ..."
Abstract
-
Cited by 5 (2 self)
- Add to MetaCart
, in many cases two or more channels are available, in which case it would be advantageous to have a multi-channel version of the algorithm. To this end, a shifted 2D Non-negative Tensor Factorisation algorithm is derived, which extends Non-negative Matrix Factor 2D Deconvolution to the multi-channel case
Non-negative 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 ..."
Abstract
-
Cited by 28 (2 self)
- Add to MetaCart
... 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
Musical Source Separation using Generalised Non-Negative Tensor Factorisation models
"... A shift-invariant non-negative 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 ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
A shift-invariant non-negative 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
Sound Source Separation using Shifted Non-negative Tensor Factorisation
- Proceedings on the IEE Conference on Audio and Speech Signal Processing (ICASSP
, 2006
"... Recently, shifted Non-negative 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 ..."
Abstract
-
Cited by 15 (0 self)
- Add to MetaCart
. To this end, a shifted Non-negative Tensor Factorisation algorithm is derived, which extends shifted Non-negative Matrix Factorisation to the multi-channel case. The use of this algorithm for multi-channel sound source separation of harmonic instruments is demonstrated. Further, it is shown that the algorithm
Extended Non-negative 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 log-frequency spectrograms to allow shift invariance in frequency which causes problems when attemp ..."
Abstract
- Add to MetaCart
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 log-frequency spectrograms to allow shift invariance in frequency which causes problems when
Semi-supervised non-negative tensor factorisation of modulation spectrograms for monaural speech separation
- in In proc. the 2014 International Joint Conference on Neural Networks
, 2014
"... This paper proposes an algorithm for separating monaural au-dio signals by non-negative tensor factorisation of modulation spectrograms. The modulation spectrogram is able to repre-sent redundant patterns across frequency with similar features, and the tensor factorisation is able to isolate these p ..."
Abstract
-
Cited by 4 (3 self)
- Add to MetaCart
This paper proposes an algorithm for separating monaural au-dio signals by non-negative tensor factorisation of modulation spectrograms. The modulation spectrogram is able to repre-sent redundant patterns across frequency with similar features, and the tensor factorisation is able to isolate
Semi-Supervised Non-Negative Tensor Factorisation of Modulation Spectrograms for Monaural Speech Separation
"... Abstract—This paper details the use of a semi-supervised 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 feature-tensor representation, based on the modulat ..."
Abstract
- Add to MetaCart
, the proposed semi-supervised method outperformed both semi-supervised non-negative matrix fac-torisation and blind non-negative modulation spectrum tensor factorisation. I.
USING TENSOR FACTORISATION MODELS TO SEPARATE DRUMS FROM POLYPHONIC MUSIC
"... This paper describes the use of Non-negative Tensor Factorisation models for the separation of drums from polyphonic audio. Im-proved separation of the drums is achieved through the incorpo-ration of Gamma Chain priors into the Non-negative Tensor Fac-torisation framework. In contrast to many previo ..."
Abstract
-
Cited by 5 (1 self)
- Add to MetaCart
This paper describes the use of Non-negative Tensor Factorisation models for the separation of drums from polyphonic audio. Im-proved separation of the drums is achieved through the incorpo-ration of Gamma Chain priors into the Non-negative Tensor Fac-torisation framework. In contrast to many
Generalised Coupled Tensor Factorisation
"... We derive algorithms for generalised tensor factorisation (GTF) by building upon the well-established 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 ..."
Abstract
- Add to MetaCart
family distributions including special cases such as Tweedie’s distributions corresponding to β-divergences. By bounding the step size of the Fisher Scoring iteration of the GLM, we obtain general updates for real data and multiplicative updates for non-negative data. The GTF framework is, then extended
Non-negative 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 n-dimensional tensor factorization (n-NTF) which includes the non-negative matrix factorization (NMF) as a particular case when n = 2. We motivate the use of n-NTF in three areas of data analysis: (i) connection to latent class models in statistics, (ii ..."
Abstract
-
Cited by 139 (5 self)
- Add to MetaCart
We derive algorithms for finding a nonnegative n-dimensional tensor factorization (n-NTF) which includes the non-negative matrix factorization (NMF) as a particular case when n = 2. We motivate the use of n-NTF in three areas of data analysis: (i) connection to latent class models in statistics
Results 1 - 10
of
393
