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