Independent component analysis applied to separation of audio signals. / Análise de componentes independentes aplicada à separação de sinais de áudio.

AUTOR(ES)
DATA DE PUBLICAÇÃO

2008

RESUMO

This work studies Independent Component Analysis (ICA) for instantaneous mixtures, applied to audio signal (source) separation. Three instantaneous mixture separation algorithms are considered: FastICA, PP (Projection Pursuit) and PearsonICA, presenting two common basic principles: sources must be statistically independent and non-Gaussian. In order to analyze each algorithm separation capability, two groups of experiments were carried out. In the first group, instantaneous mixtures were generated synthetically from predefined audio signals. Moreover, instantaneous mixtures were generated from specific signal generated with special features, synthetically, enabling the behavior analysis of the algorithms. In the second group, convolutive mixtures were probed in the acoustics laboratory of LPS at EPUSP. The PP algorithm is proposed, based on the Projection Pursuit technique usually applied in exploratory and clustering environments, for separation of multiple sources as an alternative to conventional ICA. Although the PP algorithm proposed could be applied to separate sources, it couldnt be considered an ICA method, and source extraction is not guaranteed. Finally, experiments validate the studied algorithms.

ASSUNTO(S)

cocktail party higher order statistics processamento de sinais projection pursuit blind source separation independent component analysis separação cega de fontes estatística de ordem superior signal processing análise em componentes independentes busca de projeção cocktail party

ACESSO AO ARTIGO

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