Published in Preprint, 2023
D. H. Shmuel, J. P. Merkofer, G. Revach, R. J. G. van Sloun, and N. Shlezinger, “SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation,” Preprint, 2023.
Published in ICASSP, 2023
Deep Root MUSIC (DR‑MUSIC) augments the classical Root‑MUSIC algorithm with a neural model applied to the empirical autocorrelation matrix to improve performance under source coherence, low SNR, and snapshot limitations.
D. H. Shmuel, J. P. Merkofer, G. Revach, R. J. G. van Sloun, and N. Shlezinger, “Deep Root MUSIC Algorithm for Data‑Driven DoA Estimation,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
Published in Magn Reson Med., 2023
D. M. J. van de Sande, J. P. Merkofer, S. Amirrajab, M. Veta, R. J. G. van Sloun, M. J. Versluis, J. F. A. Jansen, J. S. van den Brink, M. Breeuwer, “A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow,” Magn Reson Med., 2023;1-18. doi: 10.1002/mrm.29793.
Published in Preprint, 2023
J. P. Merkofer, G. Revach, N. Shlezinger, T. Routtenberg, and R. J. G. van Sloun, “DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm,” Preprint, 2023.
Published in ICASSP, 2022
Deep Augmented MUSIC (DA-MUSIC) is hybrid model-based/data-driven DoA estimation architecture, based on the classical multiple signal classification (MUSIC) algorithm.
J. P. Merkofer, G. Revach, N. Shlezinger, and R. J. G. van Sloun, “Deep augmented music algorithm for data-driven DoA estimation,” IEEE Internatioal Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022.