Alexander Ypma

Learn More
OBJECTIVE Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing(More)
We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumbersome) manual categorization. We provide an EM algorithm for training a mixture of HMMs and show that additional static user data can be incorporated easily to possibly enhance(More)
We propose the use of blind source separation (BSS) for separation of a machine signature from distorted measurements. Based on an analysis of the mixing processes relevant for machine source separation, we indicate that instantaneous mixing may hold in acoustic monitoring. We then present a bilinear forms-based approach to instantaneous source separation.(More)
—We propose a noise estimation algorithm for single-channel noise suppression in dynamic noisy environments. A sto-chastic-gain hidden Markov model (SG-HMM) is used to model the statistics of nonstationary noise with time-varying energy. The noise model is adaptive and the model parameters are estimated online from noisy observations using a recursive(More)
For good classiication preprocessing is a key step. Good pre-processing reduces the noise in the data and retains most information needed for classiication. Poor preprocessing on the other hand can make classiication almost impossible. In this paper we evaluate several feature extraction methods in a special type of outlier detection problem, machine fault(More)
We formulate the problem of inference in nonlinear dynamical systems (NLDS) in the Expectation-Propagation framework, and propose two novel inference algorithms based on Laplace approximation and the unscented transform (UT). The algorithms are compared empirically and employed as an improved E-step in a conjugate gradient learning algorithm. We illustrate(More)
Periodogram smoothing of the received noisy signal is a challenging problem in speech enhancement. We present a Bayesian approach, where the instantaneous periodogram is smoothed through an adaptive smoothing parameter. By updating sufficient statistics using new samples of the noisy signal, the smoothing parameter is adjusted on-line. The performance of(More)