Jörn Anemüller

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Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms. We regard EEG sources as eliciting spatio-temporal activity patterns, corresponding to, e.g. trajectories of activation(More)
An eight-channel database of head-related impulse responses (HRIRs) and binaural room impulse responses (BRIRs) is introduced. The impulse responses (IRs) were measured with three-channel behind-the-ear (BTEs) hearing aids and an in-ear microphone at both ears of a human head and torso simulator. The database aims at providing a tool for the evaluation of(More)
Blind source represents a signal processing technique with a large potential for noise reduction. However, its application in modern digital hearing aids poses high demands with respect to computational efficiency and speed of adaptation towards the desired solution. In this paper, an algorithm is presented which fulfills these goals under the idealized(More)
This paper introduces the new OLdenburg LOgatome speech corpus (OLLO) and outlines design considerations during its creation. OLLO is distinct from previous ASR corpora as it specifically targets (1) the fair comparison between human and machine speech recognition performance, and (2) the realistic representation of intrinsic variabilities in speech that(More)
Robust detection of speech embedded in real acoustic background noise is considered using an approach based on subband amplitude modulation spectral (AMS) features and trained discriminative classifiers. Performance is evaluated in particular for situations in which speech is embedded in acoustic backgrounds not presented during classifier training, and for(More)
The current study presents an analysis of the robustness of a speech detector in real background sounds. One of the most important aspects of automatic speech/nonspeech classification is robustness in the presence of strongly varying external conditions. These include variations of the signal-to-noise ratio as well as fluctuations of the background noise.(More)
In this paper, an acoustic event detection system is proposed. It consists of a noise reduction signal enhancement step based on the noise power spectral density estimator proposed in [1] and on the noise suppression by [2], a Gabor filterbank feature extraction stage and a two layer hidden Markov model as back-end classifier. Optimization on the(More)
Temporal variability of neuronal response characteristics during sensory stimulation is a ubiquitous phenomenon that may reflect processes such as stimulus-driven adaptation, top-down modulation or spontaneous fluctuations. It poses a challenge to functional characterization methods such as the receptive field, since these often assume stationarity. We(More)