Application of the Minimum Fuel Network to Music Signals

Anders La-Cour Harbo

Application of the Minimum Fuel Network to Music Signals

Publikation: Working paper/Preprint › Working paper › Forskning › peer review

Abstract

Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN) for finding sparse representations of music signals. This method is a set of two ordinary differential equations. We argue that the most important parameter for optimal use of this method is the discretization step size, and we demonstrate that this can be a priori determined. This significantly speeds up the convergence of the MFNN to the optimal sparse solution.

Originalsprog	Engelsk
Status	Udgivet - 2003

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

WAVES: WAVES / Wavelets in Audio/Visual Electronic Systems
Stoustrup, J. (PI (principal investigator)), la Cour-Harbo, A. (Projektdeltager), Andersen, P. (Projektdeltager), Pedersen, T. S. (Projektdeltager), Odgaard, P. F. (Projektdeltager), Vidal Sanchez, E. (Projektdeltager), Bendtsen, J. D. (Projektdeltager), Christensen, H. V. (Projektdeltager), Endelt, L. Ø. (Projektdeltager), Borup, L. (Projektdeltager), Nielsen, M. (Projektdeltager), Hansen, P. C. (Projektdeltager), Christensen, O. (Projektdeltager), Søndergaard, P. L. (Projektdeltager), Skjølstrup, C. E. (Projektdeltager), Verbraak, B. (Projektdeltager), Fløe Mikkelsen, H. (Projektdeltager), Nissen, P. A. (Projektdeltager), Madsen, O. (Projektdeltager) & Thorsen, P. (Projektdeltager)
Danish Technical Research Foundation
30/06/2007 → 01/09/2009
Projekter: Projekt › Forskning

Citationsformater

@techreport{39cd78709c2d11db8ed6000ea68e967b,

title = "Application of the Minimum Fuel Network to Music Signals",

abstract = "Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN) for finding sparse representations of music signals. This method is a set of two ordinary differential equations. We argue that the most important parameter for optimal use of this method is the discretization step size, and we demonstrate that this can be a priori determined. This significantly speeds up the convergence of the MFNN to the optimal sparse solution.",

author = "Harbo, {Anders La-Cour}",

year = "2003",

language = "English",

type = "WorkingPaper",

}

TY - UNPB

T1 - Application of the Minimum Fuel Network to Music Signals

AU - Harbo, Anders La-Cour

PY - 2003

Y1 - 2003

N2 - Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN) for finding sparse representations of music signals. This method is a set of two ordinary differential equations. We argue that the most important parameter for optimal use of this method is the discretization step size, and we demonstrate that this can be a priori determined. This significantly speeds up the convergence of the MFNN to the optimal sparse solution.

AB - Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN) for finding sparse representations of music signals. This method is a set of two ordinary differential equations. We argue that the most important parameter for optimal use of this method is the discretization step size, and we demonstrate that this can be a priori determined. This significantly speeds up the convergence of the MFNN to the optimal sparse solution.

M3 - Working paper

BT - Application of the Minimum Fuel Network to Music Signals

ER -

Application of the Minimum Fuel Network to Music Signals

Abstract

AUB Link

Projekter

WAVES: WAVES / Wavelets in Audio/Visual Electronic Systems

Citationsformater