M
Marc Moonen
Researcher at Katholieke Universiteit Leuven
Publications - 846
Citations - 18911
Marc Moonen is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Equalization (audio) & Noise reduction. The author has an hindex of 66, co-authored 796 publications receiving 17837 citations. Previous affiliations of Marc Moonen include Catholic University of Leuven & University of Nice Sophia Antipolis.
Papers
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Optimal training design for MIMO OFDM systems in mobile wireless channels
TL;DR: It is shown that the optimal pilot sequences derived in this paper outperform both the orthogonal and random pilot sequences and that a considerable gain in signal-to-noise ratio (SNR) can be obtained by using the RLS algorithm, especially in slowly time-varying channels.
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On- and off-line identification of linear state-space models
TL;DR: This matrix algorithm for the identification of statespace models for multivariable linear time-invariant systems using (possibly noisy) input-output (I/O) measurements only draws its excellent performance from repeated use of the numerically stable and accurate singular value decomposition.
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Optimal multiuser spectrum balancing for digital subscriber lines
TL;DR: The proposed centralized algorithm uses the dual decomposition method to optimize spectra in an efficient and computationally tractable way and shows significant performance gains over existing dynamics spectrum management techniques.
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GSVD-based optimal filtering for single and multimicrophone speech enhancement
Simon Doclo,Marc Moonen +1 more
TL;DR: Simulations show that the GSVD-based optimal filtering technique has a better performance than standard fixed and adaptive beamforming techniques for all reverberation times and that it is more robust to deviations from the nominal situation, as, e.g., encountered in uncalibrated microphone arrays.
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Joint DOA and multi-pitch estimation based on subspace techniques
TL;DR: A novel method for high-resolution joint direction-of-arrivals (DOA) and multi-pitch estimation based on subspaces decomposed from a spatio-temporal data model is presented, termed multi-channel harmonic MUSIC (MC-HMUSIC).