In this paper, we propose a new classification method using composite features, each of which consists of a number of primitive features. The covariance of two composite features contains information on statistical dependency among multiple primitive features. A new discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (...

FACULTY OF ENGINEERING, SCIENCE AND MATHEMATICS SCHOOL OF ELECTRONICS AND COMPUTER SCIENCE Doctor of Philosophy MUTUAL FEATURES FOR PATTERN CLASSIFICATION by Heiko Claussen The mean of a data set is one trivial representation of data from one class. This thesis discusses mutual interdependence analysis (MIA) that is successfully used to extract more involved representations, or “mutual features...

Spectral decomposition, to this day, still remains the main analytical paradigm for the analysis of EEG oscillations. However, conventional spectral analysis assesses the mean characteristics of the EEG power spectra averaged out over extended periods of time and/or broad frequency bands, thus resulting in a "static" picture which cannot reflect adequately the underlying neurodynamic. A relativ...

Observation of speech spectrum leads to the fact that speech has a specific spectral fluctuation pattern both along time and frequency. In this paper, we integrate the usage of this nature in a multi-feature approach for voice activity detection. The effect of separating such specific spectral fluctuation using multi-stage HPSS (Harmonic-Percussive Sound Separation) has been analyzed over conve...