Audio source separation using independent component analysis and beam formation (Paperback)

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.

General

Imprint:	Grin Verlag
Country of origin:	United States
Release date:	February 2014
First published:	February 2014
Authors:	Kishan Panaganti
Dimensions:	210 x 148 x 2mm (L x W x T)
Format:	Paperback - Trade
Pages:	32
ISBN-13:	978-3-656-58886-3
Categories:	Books > Professional & Technical > Technology: general issues > General Books > Science & Mathematics > Physics > Classical mechanics > Sound, vibration & waves (acoustics) Promotions
LSN:	3-656-58886-4
Barcode:	9783656588863

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