Audio source separation using independent component analysis and beam formation (Paperback) (ISBN-13: 9783656588863)

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.

Author: Kishan Panaganti
ISBN-10: 3656588864
ISBN-13: 9783656588863
Publisher: Grin Verlag
Language: English
Published: 02/18/2014
Pages: 32
Format: Paperback
Weight: 0.12lbs
Size: 8.27h x 5.83w x 0.08d