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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)

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)

Blind source separation is commonly based on maximizing measures related to independence of estimated sources such as mutual statistical independence assuming non-Gaussian distributions, decorrelation at different time-lags assuming spectral differences or decorrelation assuming source non-stationarity. Here, the use of an alternative model for source… (More)

- Francesco Nesta, Shoko Araki, Tomohiro Nakatani, Hiroshi Sawada, Pejman Mowlaee, Joonas Nikunen +19 others
- 2011

- Jörn Anemüller, Tino Gramss´ýµ
- 1999

In this paper, we propose a method for the on-line blind separation of sound sources in the case where the mixing filters have a AE-shaped impulse response. Our algorithm works entirely in the frequency domain and exhibits fast convergence due to cross-frequency couplings. Specific problems related to on-line separation of running speech are discussed. The… (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)

- Jörn Anemüller
- 2001

- Jörn Anemüller, Terrence J Sejnowski, Scott Makeig
- 2003

Independent component analysis (ICA) has proved to be a highly useful tool for modeling brain data and in particular electroencephalographic (EEG) data. In this paper, a new method is presented that may better capture the underlying source dynamics than ICA algorithms hereto employed for brain signal analysis. We suppose that a brief, impulse-like… (More)

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern of activation, which leads to the instantaneous mixing model. To allow modeling patterns of spatio-temporal dynamics, in particular, the flow of oxygenated… (More)