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PublicationsAll Publications

Journals

B. Weiss, H. T. Wu, and R. Talmon, Extracting Common Components from Partially Observed Views Using Diffusion Geometry, TMLR, 2026.
Y. I. Segman, A. Amar, and R. Talmon, Structure-Aware Matrix Pencil for Estimating the Number of Damped Complex Exponentials, accepted for publication in IEEE Transactions on Signal Processing, 2026.
A. Bar, J. Picard, I. Cohen, and R. Talmon, Domain Adaptation for DoA Estimation in Multipath Channels with Interferences, IEEE Trans. Signal Process., Vol. 73, pp. 3734 - 3747, 2025.
A. Ghanayim, H. Benisty, A. Cohen-Ramon, S. Schwartz, R. Talmon, and J. Schiller, VTA projections to M1 are essential for reorganization of layer 2-3 network dynamics underlying motor learning, Nature Communications 16.1: 200., 2025.
E. Bronstein, E. Faran, R. Talmon, and D. Shilo, Data-driven analysis of acoustic emission signals for the distinction of deformation mechanisms, Physical Review Materials, 9(7), 073804, 2025.
G. Maman and R. Talmon, Diffusion Optimal Transport for Domain Adaptation, Transactions on Machine Learning Research, 2025.
O. Katz, R. R. Lederman, and R. Talmon, Multimodal manifold learning using kernel interpolation along geodesic paths, Information Fusion, 114: 102637, 2025.
S. Desai, S. Salahuddin, R. Yusuf, K. Ranjan, J. Gu, Y.-W. Lin, R. Talmon, Y. Kluger, H. Zhao, K. Schalper, B. Emu, Spatial Analysis Reveals Impaired Immune Cell Function within the Tumor Microenvironment of HIV-associated Non-small Cell Lung Cancer, The Journal of Clinical Investigation, 135(14), 2025.
S. Y Yeh, H. T. Wu, R. Talmon, and M. P. Tsui, M. P., Landmark Alternating Diffusion, SIAM Journal on Mathematics of Data Science (SIMODS), 7(2), pp. 621-642, 2025.
D. W. Sroczynski, F. Dietrich, E. D. Koronaki, R. Talmon, R. R. Coifman, E. Bollt, and I. G. Kevrekidis, On learning what to learn: Heterogeneous observations of dynamics and establishing possibly causal relations among them, PNAS nexus, 3(12), pp. 494, 2024.
E. Bronstein, E. Faran, R. Talmon, and D. Shilo, Uncovering Avalanche Sources, Nature Communications 15.1: 7474., 2024.
J. McErlean, J. Malik, Y-T. Lin, R. Talmon, H-T. Wu, Unsupervised Ensembling of Multiple Software Sensors with Phase Synchronization: A Robust Approach For Electrocardiogram-derived Respiration, Physiological Measurement, 45.3: 035008, 2024.
N. Habouba, R. Talmon, D. Kraus, R. Farah, A. Apter, T. Steinberg, R. Radhakrishnan, D. Barazany, and T. Horowitz-Kraus, Parent-child couples display shared neural fingerprints while listening to stories, Scientific Reports, 14, no. 1, 2024.
O. Katz, R. Talmon, and Uri Shaham, Supervised Domain Adaptation Based on Marginal and Conditional Distributions Alignment, Transactions on Machine Learning Research, 2024.
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
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
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
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
G. Pai, A. Bronstein, R. Talmon, and R. Kimmel, Deep Isometric Maps, Image and Vision Computing, 123, 104461, 2022.
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
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
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
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
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
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
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
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
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
T. Shnitzer, M. Ben-Chen, L. Guibas, R. Talmon and H.-T. Wu, Recovering Hidden Components in Multimodal Data with Composite Diffusion Operators, SIAM Journal on Mathematics of Data Science, Vol. 1, No. 3, pp. 588-616, 2019
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
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.
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
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
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
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
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
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
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
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
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
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
V. Papyan, R. Talmon, Multimodal Latent Variable Analysis, Signal Processing, Vol. 142, pp. 178–187, 2018
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
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
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
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
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
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
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
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
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
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
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
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. 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
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, 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, 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
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
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

Conferences

A. Bar Sella, I. Aviram, M. Zeyde, A. Amar, and R. Talmon, Data-driven Stable State Detection in Power System, EUSIPCO, 2026.
O. Ronai, A. Bar, and R. Talmon, Subspace Method for RTF Estimation and Speech Enhancement Using Riemannian Geometry, EUSIPCO, 2026.
A. Yacobi, N. Ben-Ari, R. Talmon, and U. Shaham, Learning Shared Representations from Unpaired Data, NeurIPS, 2025.
E. Lavi, A. Bourvine, Y.-W. Lin, and R. Talmon, Hyperbolic Distance Based on EMD and Diffusion for Hyperspectral Imaging, ICASSP, 2025.
H. Mendelman and R. Talmon, Supervised and Semi-Supervised Diffusion Maps with Label-Driven Diffusion, ICLR, 2025.
I. Cohen and R. Talmon, Spectral Graph Coarsening Using Inner Product Preservation and the Grassmann Manifold, NeurIPS, 2025.
O. Ronai, Y. Sitton, A. Bar, and R. Talmon, RTF Estimation Using Riemannian Geometry for Speech Enhancement in The Presence of Interferences, ICASSP, 2025.
T. Dages, S. Weber, Y.-W. Lin, R. Talmon, D. Cremers, M. Lindenbaum, A. M. Bruckstein, and R. Kimmel, Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding, In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pp. 25842-25853. 2025.
Y. I. Segman, A. Amar, and R. Talmon, SAMP++: Robust Structure-Aware Matrix Pencil, EUSIPCO, 2025.
Y.-W. Lin, R. R. Coifman, G. Mishne, and R. Talmon, Hyperbolic Diffusion Tree-Wasserstein Distance for Data with Latent Feature Hierarchy, ICLR, 2025.
Y.-W. Lin, R. R. Coifman, G. Mishne, and R. Talmon, Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance, NeurIPS, 2025 (Spotlight).
A. Bar, R. Mulayoff, T. Michaeli, and R. Talmon, The Expected Loss of Preconditioned Langevin Dynamics Reveals the Hessian Rank, accepted for publication in AAAI, 2024
D. Cohen, T. Shnitzer, Y. Kluger, and R. Talmon, Few-Sample Feature Selection via Feature Manifold Learning, ICML, 2023
J. Picard, A. Bar, and R. Talmon, Direct Position Determination by Covariance-Fitting on the Riemannian Manifold of Hermitian Positive Definite Matrices, accepted for publication in ICASSP, 2024
T. B. Yampolsky, O. Lindenbaum, and R. Talmon, Domain and Modality Adaptation Using Multi-Kernel Matching, EUSIPCO, 2023
Y.-W. Lin, R. R. Coifman, G. Mishne, and R. Talmon, Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning, ICML, 2023
Y.-W. Lin, Y. Kluger, and R. Talmon, Hyperbolic Diffusion Procrustes Analysis for Intrinsic Representation of Hierarchical Data Sets, accepted for publication in ICASSP, 2024
Y.-W. Lin, R. Talmon, and R. Levie, Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters, NeurIPS, 2024.
U. Shaham, J. Svirsky, O. Katz, and R. Talmon, Discovery of Single Independent Latent Variable, NeurIPS, 2022
Y.-W. Lin, Y. Kluger, and R. Talmon, Hyperbolic Procrustes Analysis Using Riemannian Geometry, Proc. NeurIPS 2021
L. Aloni, O. Bobrowski, and R. Talmon, Joint geometric and topological analysis of Hierarchical datasets, Proc. ECML-PKDD 2021
O. Rahamim and R. Talmon, Aligning Sets of Temporal Signals with Riemannian Geometry and Koopman Operator, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’21), May 2021
P. Lifshits and R. Talmon, Unsupervised acoustic condition monitoring with Riemannian geometry, Proc. IEEE Int. Workshop Machine Learning for Signal Processing (MLSP), 2020
A. Bar, R. Talmon, and R. Meir, Option Discovery in the Absence of Rewards with Manifold Analysis, Proc. of the 37th International Conference on Machine Learning, Online, PMLR 119, 2020.
L. Forster, T. Shnitzer , A. Schmidt, R. Talmon, and W. Kellermann, Diffusion maps Particle filter, EUSIPCO 2019, A Coruna, Spain, Sept 2019
A. Brendel, B. Laufer-Goldshtein, S. Gannot, R. Talmon, and W. Kellermann, Localization of unknown number of speakers in adverse conditions using reliability information and diarization, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’19), Brighton, UK, May 2019
G. Maman, O. Yair, D. Eytan, and R. Talmon, Domain adaptation using Riemannian geometry of SPD matrices, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’19), Brighton, UK, May 2019
G. Pai, R. Talmon, A. Bronstein, and R. Kimmel, DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling, Proc. IEEE WACV 2019
R. Dorfman, E. Wagner, A. Lahav, A. Amar, R. Talmon, and Y. Halle, Spatio-Temporal Detection of Cumulonimbus Clouds in Infrared Satellite Images, (Best student paper award), Proc. of IEEE ICSEE, 2018
B. Laufer-Goldshtein, R. Talmon and S. Gannot, Diarization and Separation Based on a Data-Driven Simplex, Proc. IEEE 26th European Signal Processing Conference (EUSIPCO), pp. 842-846, 2018
A. Levis, R. Talmon, Y. Schechner, Statistical tomography of microscopic life, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6411-6420, 2018
O. Yair, E. Lustig, R. Talmon, M. Segev, Classifying Photonic Topological Phases Using Manifold Learning, Conference on Lasers and Electro-Optics (CLEO), paper FM1E.6, 2018
B. Laufer-Goldshtein, R. Talmon, I. Cohen and S. Gannot, Multi-view source localization based on power ratios, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’18), Calgary, Canada, Apr. 2018
T. Shnitzer, M. Rapaport, N. Cohen, N. Yarovinsky, R. Talmon and J. Aharon-Peretz, Alternating diffusion maps for dementia severity assessment, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’17), New Orleans, USA, Mar. 2017
B. Laufer-Goldshtein, R. Talmon and S. Gannot, Speaker tracking on multiple-manifolds with distributed microphones, LVA/ICA, Feb. 2017
D. Dov, R. Talmon and I. Cohen, Kernel method for speech source activity detection in multi-modal signals, Proc. IEEE ICSEE 2016 – International Conference on the Science of Electrical Engineering, Eilat, Israel, Nov. 2016
B. Laufer-Goldshtein, R. Talmon and S. Gannot, A real-life experimental study on semi-supervised source localization based on manifold learning, Proc. IEEE ICSEE 2016 – International Conference on the Science of Electrical Engineering, Eilat, Israel, Nov. 2016
O. Yair, R. Talmon, Multimodal metric learning with local CCA, Proc. SSP, 2016
R. Amit, G. Mishne, R. Talmon, Improving resolution in supervised patch-based target detection, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’16), Shanghai, China, Mar. 2016
B. Laufer, R. Talmon, and S. Gannot, Manifold-based Bayesian inference for semi-supervised source localisation, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’16), Shanghai, China, Mar. 2016 (invited paper)
B. Laufer, R. Talmon, and S. Gannot, A Study on Manifolds of Acoustic Responses, Proc. of the 12th International Conference on Latent Variable Analysis and Signal Separation, Czech Republic, Aug. 2015
R. R. Lederman, R. Talmon, H.-T. Wu, Y.-L. Lo, and R. R. Coifman, Alternating diffusion for common manifold learning with application to sleep stage assessment, Proc. 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’15), Brisbane, Australia, Apr. 2015
V. Chudacek, R. Talmon, J. Anden, S. Mallat, R. R. Coifman, P. Abry, and M. Doret, Low Dimensional Manifold Embedding for Scattering Coefficients of Intrapartum Fetal Heart Rate Variability, Proc. 36th Annual Internat. Conf. of the IEEE Engineeing in Medicine and Biology Society, EMBC-2014, Chicago, IL, Aug. 2014
B. Laufer, R. Talmon, S. Gannot, Relative Transfer Function Modeling for Supervised Source Localisation, (Runner up - best student paper award), Proc. IEEE Workshop on Application of Signal Processing to Audio and Acoustics, WASPAA-2013, New Paltz, NY, Oct. 2013
R. Talmon and S. Gannot, Relative Transfer Function Identification on Manifold for Supervised GSC Beamformers, Proc. 21th European Signal Processing Conference, EUSIPCO-2013 (invited paper), Marrakech, Morocco, Sept. 9 – 13, 2013
R. Talmon, I. Cohen, S. Gannot and R. R. Coifman, Graph-based Bayesian Approach for Transient Interference Suppression, Proc. 12th European Signal Processing Conference, EUSIPCO-2013 (invited paper), Marrakech, Morocco, Sept. 9 – 13, 2013
R. Talmon, Y. Shkolinsky and R. R. Coifman, Nonlinear Modeling and Processing Using Empirical Intrinsic Geometry with Application to Biomedical Imaging, Proc. SEE Internat. Conf. Geometric Science of Information, GSI-2013 (invited paper), Paris, France, Aug. 28 – 30, 2013
R. Talmon and E.A.P. Habets, Blind Reverberation Time Estimation by Intrinsic Modeling of Reverberant Speech, Proc. 38th IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2013, Vancouver, Canada, May 26 – 31, 2013
A. Hirszhorn, D. Dov, R. Talmon, and I. Cohen, Transient Interference Suppression in Speech Signals Based on the OM-LSA Algorithm, Proc. Internat. Workshop Acoust. Signal Enhancement, IWAENC-2012, Aachen, Germany, September 4 – 6, 2012 software
T. Koren, R. Talmon and I. Cohen, Supervised System Identification Based on Local PCA Models, Proc. 37th IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2012, Kyoto, Japan, March 25 – 30, 2012
Y. Michalevsky, R. Talmon and I. Cohen, Speaker Identification Using Diffusion Maps, Proc. 19th European Signal Processing Conference, EUSIPCO-2011, Barcelona, Spain, August 29 – September 2, 2011
R. Talmon, I. Cohen and S. Gannot, Clustering and Suppression of Transient Noise in Speech Signals Using Diffusion Maps, Proc. 36th IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2011, Prague, Czech Republic, May 22 – 27, 2011
R. Talmon, I. Cohen and S. Gannot, Speech Enhancement in Transient Noise Environment Using Diffusion Filtering, Proc. 35th IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2010, Dallas, Texas, March 14 -19, 2010
R. Talmon, I. Cohen and S. Gannot, Multichannel Speech Enhancement Using Convolutive Transfer Function Approximation in Reverberant Environments, Proc. 34th IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2009, Taipei, Taiwan, April 19 –24, 2009, pp. 3885-3888
R. Talmon, I. Cohen and S. Gannot, Identification of the Relative Transfer Function Between Microphones in Reverberant Environments, Proc. 25th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI-2008, Eilat, Israel, 3-5 December 2008, pp. 208-212
R. Talmon, I. Cohen and S. Gannot, Supervised Source Localization Using Diffusion Kernels, Proc. IEEE Workshop on Application of Signal Processing to Audio and Acoustics, WASPAA-2011, New Paltz, NY, October 16 – 19, 2011

Books

B. Laufer-Goldshtein, R. Talmon, and S. Gannot, Data-Driven Multi-Microphone Speaker Localization on Manifolds, Foundations and Trends in Signal Processing, Now, 2020

Book Chapters

T. Shnitzer, R. Talmon, J. J. Slotine, Manifold Learning for Data-Driven Dynamical System Analysis, in A. Mauroy, I. Mezic, Y. Susuki (Eds.), The Koopman Operator in Systems and Control, Springer, 2019
T. Shnitzer, R. Lederman, R. Talmon, G. R. Liu, H. T. Wu, Diffusion operators for multimodal data analysis, in R. Kimmel and X.-C. Tai (Eds.), Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2:, Handbook of Numerical Analysis, Vol. 20, Elsevier, 2019
D. Dov, R. Talmon, and I. Cohen, Audio-visual Source Separation with Alternating Diffusion Maps, Audio Source Separation, Springer, 2018
R. R. Coifman, R. Talmon, M. Gavish, and A. Haddad, Information Integration/Organization and Numerical Harmonic Analysis, Springer Proceedings of AMMCS-2013
R. Talmon, I. Cohen and S. Gannot, Identification of the Relative Transfer Function between Sensors in the Short-Time Fourier Transform Domain, in I. Cohen, J. Benesty and S. Gannot (Eds.), Speech Processing in Modern Communication: Challenges and Perspectives, Springer, 2010, Ch. 2, pp. 33-48

Magazines

R. Talmon, I. Cohen, S. Gannot, and R. R. Coifman, Diffusion Maps for Signal Processing: A Deeper Look at Manifold-Learning Techniques Based on Kernels and Graphs, Special Issue of IEEE Signal Processing Magazine on Advances in Kernel-based Learning for Signal Processing (invited review paper), Vol. 30, Issue 4, pp. 75-86, Jul. 2013
R. Talmon, I. Cohen, S. Gannot, and R. R. Coifman, Diffusion maps for signal processing, Special Issue on Advances in Kernel-based Learning for Signal Processing in the IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 75-86, 2013 (invited paper).
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