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Audio source separation using independent component analysis and beam formation - Couverture souple

Panaganti, Kishan

 
9783656588863: Audio source separation using independent component analysis and beam formation
Couverture souple
ISBN 10 : 3656588864 ISBN 13 : 9783656588863
Editeur : Grin Verlag, 2014

Synopsis

Project Report from the year 2013 in the subject Audio Engineering, grade: 10, , course: ECE, language: English, abstract: Audio source separation is the problem of automated separation of audio sources present in a room, using a set of differently placed microphones, capturing the auditory scene. The whole problem resembles the task a human can solve in a cocktail party situation, where using two sensors (ears), the brain can focus on a specific source of interest, suppressing all other sources present (cocktail party problem). For computational and conceptual simplicity this problem is often represented as a linear transformation of the original audio signals. In other words, each component (multivariate signal) of the representation is a linear combination of the original variables (original subcomponents). In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents by assuming that the subcomponents are non-Gaussian signals and that they are all statistically independent from each other. Such a representation seems to capture the essential structure of the data in many applications. Here we separate audio using different criteria suggested for ICA, being PCA (Principal Component Analysis), Non-gaussianity maximization using kurtosis and neg-entropy methods, frequency domain approach using non-gaussianity maximization and beamforming.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de la quatrième de couverture

Project Report from the year 2013 in the subject Audio Engineering, grade: 10, , course: ECE, language: English, abstract: Audio source separation is the problem of automated separation of audio sources present in a room, using a set of differently placed microphones, capturing the auditory scene. The whole problem resembles the task a human can solve in a cocktail party situation, where using two sensors (ears), the brain can focus on a specific source of interest, suppressing all other sources present (cocktail party problem). For computational and conceptual simplicity this problem is often represented as a linear transformation of the original audio signals. In other words, each component (multivariate signal) of the representation is a linear combination of the original variables (original subcomponents). In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents by assuming that the subcomponents are non-Gaussian signals and that they are all statistically independent from each other. Such a representation seems to capture the essential structure of the data in many applications. Here we separate audio using different criteria suggested for ICA, being PCA (Principal Component Analysis), Non-gaussianity maximization using kurtosis and neg-entropy methods, frequency domain approach using non-gaussianity maximization and beamforming.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Grin Verlag
Date d'édition
2014
Langue
anglais
ISBN 10 
3656588864
ISBN 13 
9783656588863
Reliure
Broché
Nombre de pages
32
Coordonnées du fabricant
GRIN Publishing GmbH
info@grin.com

Waltherstraße 23
München
80337
Allemagne