Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches

In this thesis, we propose two new machine learning schemes, a subband-based Independent Component Analysis scheme and a hybrid Independent Component AnalysisSupport Vector Machine scheme, and apply them to the problems of blind acoustic signal separation and face detection. Based on a linear model, classical Independent Component Analysis ICA provides a method of representing data as independent components. In contrast to Principal Component Analysis PCA, which decorrelates the data based on its covariance matrix, ICA uses higher-order statistics of the data to minimize the dependence between the components of the system output. An important application of ICA is blind source separation. However, classical ICA algorithms do not work well for separation in the presence of noise or when performed on-line. Inspired by the psychoacoustic discovery that humans perceive and process acoustic signals in different frequency bands independently, we propose a new algorithm, subband-based ICA, that integrates ICA with time-frequency analysis to separate mixed signals. In subband-based ICA, the separations are performed in parallel in several frequency bands. Wavelet decomposition and best basis selection in waveletDCT packets can be incorporated into this algorithm.