Description and generation of spherically invariant speech-model signals
Signal Processing
Discrete-time signal processing
Discrete-time signal processing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Separation of real-world signals
Signal Processing - Special issue on acoustic echo and noise control
Estimation of time delays between unknown colored signals
Signal Processing
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
On-line Convolutive Blind Source Separation of Non-Stationary Signals
Journal of VLSI Signal Processing Systems
Blind separation of delayed sources based on information maximization
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Complex independent component analysis of frequency-domain electroencephalographic data
Neural Networks - Special issue: Neuroinformatics
Improvement of the Initialization of ICA Time-Frequency Algorithms for Speech Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
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Blind source separation 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 assumption that the superposition of sources in rooms can be approximated as a superposition under anechoic conditions. Specifically, attenuation, the signals' finite propagation speed, and diffuse noise are accounted for, whereas reflections and reverberation are considered as negligible effects. This approximation is referred to as the 'free field' assumption. Starting from a general blind source separation algorithm for Fourier transformed speech signals, the free field assumption is incorporated into the framework, yielding a simple, fast and adaptive algorithm that is able to track moving sources. Implementation details are given which were found to be indispensable for fast and robust signal separation. Performance is evaluated both by simulations and experimentally, including separation of a moving and a fixed speaker in a recorded real anechoic environment. The potential benefits and shortcomings of this algorithm are discussed with regard to its inclusion into the signal processing framework of digital hearing aids for real reverberant acoustic situations.