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Array Signal Processing

A. Bar and R. Talmon, On Interference-Rejection using Riemannian Geometry for Direction of Arrival Estimation, IEEE Transactions on Signal Processing, Vol. 72, pp. 260-274, 2024

Nonlinear Data and Signal Analysis with Diffusion Operators (DIFFOP)

A. Lahav and R. Talmon, Procrustes Analysis on the Manifold of SPSD Matrices for Data Sets Alignment, IEEE Transactions on Signal Processing, vol. 71, pp. 1907-1921, 2023
A. Bar and R. Talmon, On Interference-Rejection using Riemannian Geometry for Direction of Arrival Estimation, IEEE Transactions on Signal Processing, Vol. 72, pp. 260-274, 2024
T. Shnitzer, H.-T. Wu, and R. Talmon, Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators, Applied and Computational Harmonic Analysis, Vol. 68, 101583, 2024
O. Katz, R. R. Lederman, and R. Talmon, Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing, submitted arxiv
Y.-W. E. Lin, T. Shnitzer, R. Talmon, F. Villarroel-Espindola, S. Desai, K. Schalper, and Y. Kluger, Graph of graphs analysis for multiplexed data with application to imaging mass cytometry, PLoS computational biology, Vol. 17, No. 3, e1008741, 2021
O. Yair, A. Lahav, and R. Talmon, Symmetric Positive Semi-definite Riemannian Geometry with Application to Domain Adaptation, submitted. arxiv
E. Lustig, O. Yair, R. Talmon, M. Segev, Identifying Topological Phase Transitions in Experiments Using Manifold Learning, Physical Review Letters, Vol. 125, No. 12, 127401, 2020
F. Dietrich, O. Yair, R. Mulayoff, R. Talmon, and I. G. Kevrekidis, Spectral Discovery of Jointly Smooth Features for Multimodal Data, SIAM Journal on Mathematics of Data Science, 4(1), 410-430, 2022
O. Yair, F. Dietrich, I. G. Kevrekidis, and R. Talmon, Domain Adaptation with Optimal Transport on the Manifold of SPD matrices, submitted. arxiv

Riemannian Geometry

A. Lahav and R. Talmon, Procrustes Analysis on the Manifold of SPSD Matrices for Data Sets Alignment, IEEE Transactions on Signal Processing, vol. 71, pp. 1907-1921, 2023
A. Bar and R. Talmon, On Interference-Rejection using Riemannian Geometry for Direction of Arrival Estimation, IEEE Transactions on Signal Processing, Vol. 72, pp. 260-274, 2024
T. Shnitzer, H.-T. Wu, and R. Talmon, Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators, Applied and Computational Harmonic Analysis, Vol. 68, 101583, 2024
O. Katz, R. R. Lederman, and R. Talmon, Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing, submitted arxiv
O. Yair, A. Lahav, and R. Talmon, Symmetric Positive Semi-definite Riemannian Geometry with Application to Domain Adaptation, submitted. arxiv
O. Yair, F. Dietrich, I. G. Kevrekidis, and R. Talmon, Domain Adaptation with Optimal Transport on the Manifold of SPD matrices, submitted. arxiv
F. Aeed, T. Shnitzer, R. Talmon, Y. Schiller, Layer and cell specific recruitment dynamics during epileptic seizures in-vivo in Annals of Neurology, Annals of Neurology, Vol. 87, No. 1, pp. 97-115, 2020

Audio and Acoustic Signal Processing

B. Laufer-Goldstein, R. Talmon, S. Gannot, Global and Local Simplex Representations for Multichannel Source Separation, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 28, pp. 914 - 928, 2020
B. Laufer-Goldstein, R. Talmon, S. Gannot, Audio source separation by activity probability detection with maximum correlation and simplex geometry, EURASIP Journal on Audio, Speech, and Music Processing, No. 1, pp. 1-16, 2021
B. Laufer-Goldstein, R. Talmon, S. Gannot, Source Counting and Separation Based on Simplex Analysis, IEEE Transactions on Signal Processing, Volume: 66 , Issue: 24 , 6458 - 6473, 2018
D. Dov, R. Talmon and I. Cohen, Sequential audio-visual correspondence with alternating diffusion kernels, IEEE Transactions on Signal Processing, Vol. 66, No. 12, pp. 3100-3111, 2018
B. Laufer-Goldstein, R. Talmon, S. Gannot, A Hybrid Approach for Speaker Tracking based on TDOA and Data-Driven Models, IEEE/ACM Trans. Audio, Speech, Lang. Proces., Vol. 26, No. 4, pp. 725-735, 2018
B. Laufer-Goldstein, R. Talmon, S. Gannot, Semi-supervised source localization on multiple-manifolds with distributed microphones, IEEE/ACM Trans. Audio, Speech, Lang. Proces., Vol. 25, No. 7, pp. 1477-1491, Jul. 2017
D. Dov, R. Talmon and I. Cohen, Multimodal Kernel Method for Activity Detection of Sound Sources, IEEE/ACM Trans. Audio, Speech, Lang. Proces., Vol. 25, No. 6, pp. 1322 – 1334, Jun. 2017
D. Dov, R. Talmon, I. Cohen, Kernel method for voice activity detection in the presence of transients, IEEE Trans. Audio Speech Lang. Process., Vol. 24, No. 12,pp. 2313-2326, Dec. 2016
B. Laufer-Goldstein, R. Talmon, S. Gannot, Semi-supervised sound source localization based on manifold regularization, IEEE Trans. Audio, Speech and Language Processing, Vol. 24, No. 8, pp. 1393-1407, Aug. 2016
R. Talmon, I. Cohen and S. Gannot, Single-Channel Transient Interference Suppression with Diffusion Maps, IEEE Trans. Audio, Speech and Language Processing, Vol. 21, Issue 1, pp. 132-144, Jan. 2013
R. Talmon, I. Cohen, S. Gannot and R. R. Coifman, Supervised Graph-based Processing for Sequential Transient Interference Suppression, IEEE Trans. Audio, Speech and Language Processing, Vol. 20, Issue 9, pp. 2528-2538, Nov. 2012
R. Talmon, I. Cohen and S. Gannot, Transient Noise Reduction Using Nonlocal Diffusion Filters, IEEE Trans. Audio, Speech and Language Processing, Vol. 19, Issue 6, pp. 1584-1599, Aug. 2011
R. Talmon, I. Cohen and S. Gannot, Convolutive Transfer Function Generalized Sidelobe Canceler, IEEE Trans. Audio, Speech and Language Processing, Vol. 17, Issue 7, pp. 1420-1434, Sep. 2009
R. Talmon, I. Cohen and S. Gannot, Relative Transfer Function Identification Using Convolutive Transfer Function Approximation, IEEE Trans. Audio, Speech and Language Processing, Vol. 17, Issue 4, pp. 546-555, May 2009

Intrinsic Representations

G. Pai, A. Bronstein, R. Talmon, and R. Kimmel, Deep Isometric Maps, Image and Vision Computing, 123, 104461, 2022.
M. Gavish, P. C. Su, R. Talmon, and H. T. Wu, Optimal recovery of precision matrix for Mahalanobis distance from high-dimensional noisy observations in manifold learning, Information and Inference: A Journal of the IMA, 11(4), 1173-1202, 2022
P. Papaioannou, R. Talmon, I. Kevrekidis, and C. Siettos, Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics, Chaos: An Interdisciplinary Journal of Nonlinear Science}, Vol. 32, 083113, 2022
E. Bronstein, J. Zimmerman, E. Rabkin, E. Faran, R. Talmon, and D. Shilo, Enhancing the detection capabilities of nano-avalanches via data-driven classification of acoustic emission signals, Physical Review E, 108(4), 045001, 2023
I. Cohen, D. Valsky, and R. Talmon, Unsupervised Detection of Sub-Territories of the Subthalamic Nucleus During DBS Surgery with Manifold Learning, IEEE Trans. Biomedical Engineering, 70(4), 1286 - 1297, 2023
I. Zach, T. G. Dvorkind, and R. Talmon, Graph Signal Interpolation and Extrapolation Using Reproducing Kernel Hilbert Space over Manifold of Gaussian Mixture, Signal Processing, Vol. 216, 109308, 2024
Y.-W. E. Lin, T. Shnitzer, R. Talmon, F. Villarroel-Espindola, S. Desai, K. Schalper, and Y. Kluger, Graph of graphs analysis for multiplexed data with application to imaging mass cytometry, PLoS computational biology, Vol. 17, No. 3, e1008741, 2021
E. Lustig, O. Yair, R. Talmon, M. Segev, Identifying Topological Phase Transitions in Experiments Using Manifold Learning, Physical Review Letters, Vol. 125, No. 12, 127401, 2020
S. Levy, M. Lavzin, H. Benisty, A. Ghanayim, U. Dubin, S. Achvat, Z. Brosh, F. Aeed, B. D. Mensh, Y. Schiller., R. Meir, O. Barak, R. Talmon, A. W. Hantman, and J. Schiller, Cell-type specific outcome representation in primary motor cortex, Neuron, Vol. 107, No. 5, pp. 954-971, 2020
T. Shnitzer, R. Talmon and J. J. Slotine, Diffusion Maps Kalman Filter for a Class of Systems With Gradient Flows, IEEE Transactions on Signal Processing, Vol. 68, pp. 2739-2753.
A. Schwartz, and R. Talmon, Intrinsic isometric manifold learning with application to localization, SIAM Journal on Imaging Science, Vol. 12, No. 3, pp. 1347-1391, 2019
A. Lahav, R. Talmon, Y. Kluger, Mahalanonbis distance informed by clustering, Information and Inference: A Journal of the IMA, iay011, https://doi.org/10.1093/imaiai/iay011, 2018
C. J. Dsilva, R. Talmon, R. R. Coifman, and I. G. Kevrekidis, Parsimonious Representation of Nonlinear Dynamical Systems Through Manifold Learning: A Chemotaxis Case Study, Applied and Computational Harmonic Analysis (ACHA), Vol. 44, No.3, pp. 759-773, 2018 software
F. P. Kemeth, S. W. Haugland, F. Dietrich, T. Bertalan, Q. Li, E. M. Bollt, R. Talmon, K. Krischer, and I. G. Kevrekidis, An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning, IEEE Access, Vol. 6, pp. 77402-77413, 2018
J. Sulam, Y. Romano, R. Talmon, Dynamical system classification with diffusion embedding for ECG-based person identification, Signal Processing, Vol. 130, pp. 403-411, Jan. 2017
G. Mishne, R. Talmon, R. Meir, J. Schiller, U. Dubin, R. R. Coifman, Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery, IEEE Journal of Selected Topics in Signal Processing, Vol. 10, No. 7, pp. 1238-1253, Oct. 2016
W. Lian, R. Talmon, H. Zaveri, L. Carin, and R. R. Coifman, Multivariate Time Series Analysis and Diffusion Maps, Signal Processing, Vol. 116, pp. 13-28, Nov. 2015
R. Talmon, S. Mallat, H. Zaveri, and R. R. Coifman, Manifold Learning for Latent Variable Inference in Dynamical Systems, IEEE Trans. Signal Process., Vol. 63, No. 15, pp. 3843-3856, Aug. 2015
R. Talmon and R. R. Coifman, Intrinsic Modeling of Stochastic Dynamical Systems Using Empirical Geometry, Applied and Computational Harmonic Analysis (ACHA), Vol. 39, No. 1, pp. 138-160, Jul. 2015
G. Mishne, R. Talmon, and I. Cohen, Graph-Based Supervised Automatic Target Detection, IEEE Trans. Geoscience and Remote Sensing, Vol. 53, Issue. 5, pp. 2738-2754, May. 2015
D. Dov, R. Talmon, and I. Cohen, Audio-Visual Voice Activity Detection Using Diffusion Maps, IEEE Trans. Audio, Speech and Language Processing, Vol. 23, No. 4, pp. 732-745, Apr. 2015 software
H.-T. Wu, R. Talmon, Y.-L. Lo, Assess Sleep Stage by Modern Signal Processing Techniques, IEEE Transactions on Biomedical Engineering, Vol. 62, No. 4, pp. 1159-1168, Apr. 2015
C. J. Dsilva, R. Talmon, N. Rabin, R. R. Coifman, and I. G. Kevrekidis, Nonlinear Intrinsic Variables and State Reconstruction in Multiscale Simulations, The Journal of Chemical Physics, Vol. 139, Issue 18, pp. 184109, Oct. 2013
R. Talmon and R. R. Coifman, Empirical Intrinsic Geometry for Nonlinear Modeling and Time Series Filtering, Proceedings of the National Academy of Sciences (PNAS), Vol. 110, Issue 31, pp. 12535-12540, Jul. 2013
D. Duncan, R. Talmon, H. P. Zaveri, and R. R. Coifman, Identifying Preseizure State in Intracranial EEG Data Using Diffusion Kernels, Special Issue of Mathematical Biosciences and Engineering (MBE), Vol. 10, Issue 3, pp. 579-590, Jun. 2013
R. Talmon, D. Kushnir, R. R. Coifman, I. Cohen and S. Gannot, Parametrization of Linear Systems Using Diffusion Kernels, IEEE Transactions on Signal Processing, Vol. 60, No. 3, pp. 1159-1173, Mar. 2012

Multimodal Data Analysis and Fusion

T. Shnitzer, H.-T. Wu, and R. Talmon, Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators, Applied and Computational Harmonic Analysis, Vol. 68, 101583, 2024
O. Katz, R. R. Lederman, and R. Talmon, Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing, submitted arxiv
F. Dietrich, O. Yair, R. Mulayoff, R. Talmon, and I. G. Kevrekidis, Spectral Discovery of Jointly Smooth Features for Multimodal Data, SIAM Journal on Mathematics of Data Science, 4(1), 410-430, 2022
M. Taseska, T .van Waterschoot, E. A. P. Habets, and R. Talmon, Nonlinear Filtering with Variable-Bandwidth Exponential Kernels, IEEE Transactions on Signal Processing, Vol. 65, pp. 314-326, 2019
O. Katz, R. Talmon, Y.-L. Lo and H.-T. Wu, Alternating diffusion maps for multimodal data fusion, Information Fusion, Vol. 45, pp. 346-360, 2019 software
D. Dov, R. Talmon and I. Cohen, Sequential audio-visual correspondence with alternating diffusion kernels, IEEE Transactions on Signal Processing, Vol. 66, No. 12, pp. 3100-3111, 2018
V. Papyan, R. Talmon, Multimodal Latent Variable Analysis, Signal Processing, Vol. 142, pp. 178–187, 2018
R. Talmon and H.-T. Wu, Latent common manifold learning with alternating diffusion: analysis and applications, accepted for publication in Applied and Computational Harmonic Analysis, 2017
B. Laufer-Goldstein, R. Talmon, S. Gannot, Semi-supervised source localization on multiple-manifolds with distributed microphones, IEEE/ACM Trans. Audio, Speech, Lang. Proces., Vol. 25, No. 7, pp. 1477-1491, Jul. 2017
D. Dov, R. Talmon and I. Cohen, Multimodal Kernel Method for Activity Detection of Sound Sources, IEEE/ACM Trans. Audio, Speech, Lang. Proces., Vol. 25, No. 6, pp. 1322 – 1334, Jun. 2017
O. Yair, R. Talmon, Local Canonical Correlation Analysis for Nonlinear Common Variables Discovery, IEEE Transactions on Signal Processing, Vol. 65, No. 5, pp. 1101-1115, Mar. 2017
D. Dov, R. Talmon and I. Cohen, Kernel-based sensor fusion with application to audio-visual voice activity detection, IEEE Transactions on Signal Processing, Vol. 64, No. 24, pp. 6406-6416, Dec. 2016
R. R. Lederman, and R. Talmon, Learning the Geometry of Common Latent Variables Using Alternating Diffusion, Applied and Computational Harmonic Analysis (ACHA), Vol. 44, No. 3, pp. 509-536, 2018
D. Dov, R. Talmon, and I. Cohen, Audio-Visual Voice Activity Detection Using Diffusion Maps, IEEE Trans. Audio, Speech and Language Processing, Vol. 23, No. 4, pp. 732-745, Apr. 2015 software
H.-T. Wu, R. Talmon, Y.-L. Lo, Assess Sleep Stage by Modern Signal Processing Techniques, IEEE Transactions on Biomedical Engineering, Vol. 62, No. 4, pp. 1159-1168, Apr. 2015

Data-driven Dynamical Systems Analysis

O. Lindenbaum, A. Sagiv, G. Mishne, and R. Talmon, Kernel-based parameter estimation of dynamical systems with unknown observation functions, Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 31, No. 4, 043118, 2021
E. Bronstein, A. Wiegner, D. Shilo, and R. Talmon, The spatiotemporal coupling in delay-coordinates dynamic mode decomposition, Chaos: An Interdisciplinary Journal of Nonlinear Science}, Vol. 32, 123127, 2022
P. Papaioannou, R. Talmon, I. Kevrekidis, and C. Siettos, Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics, Chaos: An Interdisciplinary Journal of Nonlinear Science}, Vol. 32, 083113, 2022
T. Shnitzer, H.-T. Wu, and R. Talmon, Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators, Applied and Computational Harmonic Analysis, Vol. 68, 101583, 2024
E. Lustig, O. Yair, R. Talmon, M. Segev, Identifying Topological Phase Transitions in Experiments Using Manifold Learning, Physical Review Letters, Vol. 125, No. 12, 127401, 2020
T. Shnitzer, R. Talmon and J. J. Slotine, Diffusion Maps Kalman Filter for a Class of Systems With Gradient Flows, IEEE Transactions on Signal Processing, Vol. 68, pp. 2739-2753.
D. W. Sroczynski, O. Yair, R. Talmon, and I. G. Kevrekidis, Data-driven Evolution Equation Reconstruction for Parameter-Dependent Nonlinear Dynamical Systems, Israel Journal of Chemistry, Vol. 58, No. 6-7, Special Issue: Nonlinear Dynamics in Chemical Reaction Engineering, pp. 787-794, 2018
C. J. Dsilva, R. Talmon, R. R. Coifman, and I. G. Kevrekidis, Parsimonious Representation of Nonlinear Dynamical Systems Through Manifold Learning: A Chemotaxis Case Study, Applied and Computational Harmonic Analysis (ACHA), Vol. 44, No.3, pp. 759-773, 2018 software
F. P. Kemeth, S. W. Haugland, F. Dietrich, T. Bertalan, Q. Li, E. M. Bollt, R. Talmon, K. Krischer, and I. G. Kevrekidis, An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning, IEEE Access, Vol. 6, pp. 77402-77413, 2018
O. Yair, R. Talmon, R. R. Coifman, I. G. Kevrekidis, Reconstruction of normal forms by learning informed observation geometries from data, Proceedings of the National Academy of Sciences (PNAS), 201620045, 2017
T. Shnitzer, R. Talmon and J. J. Slotine, Manifold learning with contracting observers for data-driven time-series analysis, IEEE Transactions on Signal Processing, Vol. 65, No. 4, pp. 904-918, Feb. 2017
J. Sulam, Y. Romano, R. Talmon, Dynamical system classification with diffusion embedding for ECG-based person identification, Signal Processing, Vol. 130, pp. 403-411, Jan. 2017
G. Mishne, R. Talmon, R. Meir, J. Schiller, U. Dubin, R. R. Coifman, Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery, IEEE Journal of Selected Topics in Signal Processing, Vol. 10, No. 7, pp. 1238-1253, Oct. 2016
C. J. Dsilva, R. Talmon, C. W. Gear, R. R. Coifman, and I. G. Kevrekidis, Data-Driven Reduction for a Class of Multiscale Fast-Slow Stochastic Dynamical Systems, SIAM Journal on Applied Dynamical Systems, Vol. 15, No. 3, pp. 1327-1351, 2016
W. Lian, R. Talmon, H. Zaveri, L. Carin, and R. R. Coifman, Multivariate Time Series Analysis and Diffusion Maps, Signal Processing, Vol. 116, pp. 13-28, Nov. 2015
R. Talmon, S. Mallat, H. Zaveri, and R. R. Coifman, Manifold Learning for Latent Variable Inference in Dynamical Systems, IEEE Trans. Signal Process., Vol. 63, No. 15, pp. 3843-3856, Aug. 2015

Transports on Manifolds

A. Lahav and R. Talmon, Procrustes Analysis on the Manifold of SPSD Matrices for Data Sets Alignment, IEEE Transactions on Signal Processing, vol. 71, pp. 1907-1921, 2023
O. Yair, A. Lahav, and R. Talmon, Symmetric Positive Semi-definite Riemannian Geometry with Application to Domain Adaptation, submitted. arxiv
O. Yair, F. Dietrich, I. G. Kevrekidis, and R. Talmon, Domain Adaptation with Optimal Transport on the Manifold of SPD matrices, submitted. arxiv
O. Yair, M. Ben-chen, and R. Talmon, Parallel transport on the cone manifold of SPD matrices for domain adaptation, IEEE Trans. Signal Process., Vol. 67, no. 7, pp. 1797-1811, 2019

Other

G. Mishne, R. Talmon, I. Cohen, R. R. Coifman, and Y. Kluger, Data-Driven Tree Transforms and Metrics, IEEE Transactions on Signal and Information Processing over Networks, Vol. 4, No. 3, 2018
A. Shemesh, R. Talmon, O. Karp, I. Amir, M. Bar, and Y. J. Grobman, Affective response to architecture – investigating human reaction to spaces with different geometry, Architectural Science Review, Vol. 60, No. 2, pp. 116-125, 2017
E. Bronstein, L. Z. Toth, L. Daroczi, D. L. Beke, R. Talmon, and D. Shilo, Tracking twin boundary jerky motion at nanometer and microsecond scales," Advanced Functional Materials, Advanced Functional Materials, 2106573, 2021
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