Separating underdetermined convolutive speech mixtures (2008)
Michael Syskind Pedersen, Deliang Wang, Ulrik Kjems
Abstract. A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot...
Two-Microphone Separation of Speech Mixtures (2008)
Michael Syskind Pedersen, Deliang Wang, Jan Larsen, Senior Member, Ulrik Kjems
Abstract—Separation of speech mixtures, often referred to as the cocktail party problem, has been studied for decades. In many source separation tasks, the separation method is limited by the...
Two-microphone Separation of Speech Mixtures (2008)
Michael Syskind Pedersen, Deliang Wang, Jan Larsen, Senior Member, Ulrik Kjems
Abstract—Separation of speech mixtures, often referred to as the cocktail party problem, has been studied for decades. In many source separation tasks, the separation method is limited by the...
Separating underdetermined convolutive speech mixtures (2008)
Michael Syskind Pedersen, Deliang Wang, Jan Larsen, Ulrik Kjems
Abstract. A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot...
Michael Syskind Pedersen, Jan Larsen, Ulrik Kjems, Lucas C. Parra
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be...
Peter Toft, Peter Alshede Philipsen, Lars Kai Hansen, Ulrik Kjems
In this paper the basis of PET (Positron Emission Tomography) is reviewed, and it is shown that the measured signals can be modelled as the Radon transform of the desired spatial distribution of,...
Restoring functional PET Images using Anatomical MR Images (2007)
Peter Alshede Philipsen, Ulrik Kjems, Peter Toft, Lars Kai Hansen
In this paper we present a Bayesian method to enhance functional 3D PET images using apriori knowledge about the brain anatomy obtained from 3D MR images. We use a Markov Random Field as a prior...
A Survey of Convolutive Blind Source Separation Methods (2007)
Pedersen, Michael Syskind, Larsen, Jan, Kjems, Ulrik, Parra, Lucas C.
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be...
Two-microphone Separation of Speech Mixtures (2006)
Syskind Pedersen, Michael, Wang, DeLiang, Larsen, Jan, Kjems, Ulrik
Separation of speech mixtures, often referred to as the ocktail party problem, has been studied for decades. In many source separation tasks, the separation method is limited by the assumption of at...
Separating Underdetermined Convolutive Speech Mixtures (2006)
Pedersen, Michael, Wang, DeLiang, Larsen, Jan, Kjems, Ulrik
A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the...
Overcomplete Blind Source Separation by Combining ICA and Binary Time-Frequency Masking (2005)
Pedersen, Michael S., Wang, DeLiang, Larsen, Jan, Kjems, Ulrik
A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the...
Overcomplete blind source separation by combining ICA and binary time-frequency masking (2005)
Michael Syskind Pedersen, Deliang Wang, Jan Larsen, Ulrik Kjems
A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the...
Overcomplete blind source separation by combining ICA and binary time-frequency masking (2005)
Michael Syskind Pedersen, Deliang Wang, Jan Larsen, Ulrik Kjems
A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the...
On the difference between updating the mixing matrix and updating the separation matrix (2004)
Larsen, Jan, Kjems, Ulrik, Pedersen, Michael
When the ICA source separation problem is solved by maximum likelihood, a proper choice of the parameters is important. A comparison has been performed between the use of a mixing matrix and the use...
Semi-blind Source Separation Using Head-Related Transfer Functions (2004)
Pedersen, Michael, Hansen, Lars Kai, Kjems, Ulrik, Rasmussen, Karsten Bo
An online blind source separation algorithm which is a special case of the geometric algorithm by Parra and Fancourt has been implemented for the purpose of separating sounds recorded at microphones...
Semi-blind source separation using head-related transfere functions (2004)
Michael Syskind Pedersen, Ulrik Kjems, Karsten Bo Rasmussen
An online blind source separation algorithm which is a special case of the geometric algorithm by Parra and Fancourt [1] has been implemented for the purpose of separating sounds recorded at...
Generalizable singular value decomposition for ill-posed datasets. In: NIPS (2000)
Ulrik Kjems, Lars K. Hansen, Stephen C. Strother, Pet Imaging Service
We demonstrate that statistical analysis of ill-posed data sets is subject to a bias, which can be observed when projecting independent test set examples onto a basis dened by the training examples....
Mining the BrainMap database: Detection of outliers (2000)
Finn Arup Nielsen, Lars Kai Hansen, Ulrik Kjems
We describe a system for modeling BrainMap -- a neuroscience database describing activation foci reported from many neuroimaging studies. We apply machine learning techniques in the form of...
Modeling text with generalizable gaussian mixtures (2000)
Lars Kai Hansen, Sigurdur Sigurdsson, Thomas Kolenda, Finn Arup Nielsen, Ulrik Kjems, Jan Larsen
We apply and discuss generalizable Gaussian mixture (GGM) models for textmining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of...
Modeling Text With Generalizable Gaussian Mixtures (1999)
Lars Kai Hansen, Sigurdur Sigurdsson, Thomas Kolenda, Finn Arup Nielsen, Ulrik Kjems, Jan Larsen
We apply and discuss generalizable Gaussian mixture (GGM) models for textmining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of...
Modeling Text With Generalizable Gaussian Mixtures (1999)
Lars Kai Hansen, Sigurdur Sigurdsson, Thomas Kolenda, Finn Arup Nielsen, Ulrik Kjems, Jan Larsen
We apply and discuss generalizable Gaussian mixture (GGM) models for textmining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of...
Bayesian Signal Processing and Interpretation of Brain Scans (1998)
This paper addresses the problem of anatomical registration across individuals for functional \Theta 15 O water PET activation studies. A new algorithm for 3D non-linear structural registration...
Revisiting Boltzmann Learning: Parameter Estimation in Markov Random Fields (1996)
Lars Kai Hansen, Lars Nonboe Andersen, Ulrik Kjems, Jan Larsen
This contribution concerns a generalization of the Boltzmann Machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including...
Visualization of Neural Networks Using Saliency Maps (1995)
Ulrik Kjems, Lars Kai Hansen, Claus Svarer, Ian Law, ...
The saliency map is proposed as a new method for understanding and visualizing the nonlinearities embedded in feed-forward neural networks, with emphasis on the ill-posed case, where the...