Publikationer av Saikat Chatterjee
Refereegranskade
Artiklar
[1]
A. Ghosh et al., "DeepBayes—An estimator for parameter estimation in stochastic nonlinear dynamical models," Automatica, vol. 159, 2024.
[2]
A. E. Fontcuberta et al., "Forecasting Solar Cycle 25 with Physical Model-Validated Recurrent Neural Networks," Solar Physics, vol. 298, no. 1, 2023.
[3]
S. Mostafaei et al., "Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis : a longitudinal cohort study," Scientific Reports, vol. 13, no. 1, 2023.
[4]
S. Das et al., "Observability-Aware Online Multi-Lidar Extrinsic Calibration," IEEE Robotics and Automation Letters, vol. 8, no. 5, s. 2860-2867, 2023.
[5]
A. Honoré et al., "Vital sign-based detection of sepsis in neonates using machine learning," Acta Paediatrica, vol. 112, no. 4, s. 686-696, 2023.
[6]
S. Sinha et al., "A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting : Identifying High-performing Active Region Flare Indicators," Astrophysical Journal, vol. 935, no. 1, 2022.
[7]
X. Liang et al., "Decentralized learning of randomization-based neural networks with centralized equivalence," Applied Soft Computing, vol. 115, 2022.
[8]
F. Arian et al., "Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms," Journal of digital imaging, vol. 35, no. 6, s. 1708-1718, 2022.
[9]
A. M. Javid et al., "High-dimensional neural feature design for layer-wise reduction of training cost," EURASIP Journal on Advances in Signal Processing, vol. 2020, no. 1, 2020.
[10]
S. Mehrizi et al., "Online Spatiotemporal Popularity Learning via Variational Bayes for Cooperative Caching," IEEE Transactions on Communications, vol. 68, no. 11, s. 7068-7082, 2020.
[11]
A. Zaki et al., "Estimate exchange over network is good for distributed hard thresholding pursuit," Signal Processing, vol. 156, s. 1-11, 2019.
[12]
A. Venkitaraman, S. Chatterjee och P. Händel, "On Hilbert transform, analytic signal, and modulation analysis for signals over graphs," Signal Processing, vol. 156, s. 106-115, 2019.
[13]
A. Venkitaraman, S. Chatterjee och P. Händel, "Predicting Graph Signals Using Kernel Regression Where the Input Signal is Agnostic to a Graph," IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, vol. 5, no. 4, s. 698-710, 2019.
[14]
A. Zaki et al., "Greedy Sparse Learning Over Network," IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, vol. 4, no. 3, s. 424-435, 2018.
[15]
F. Ghayem et al., "Sparse Signal Recovery Using Iterative Proximal Projection," IEEE Transactions on Signal Processing, vol. 66, no. 4, s. 879-894, 2018.
[16]
A. Zaki, S. Chatterjee och L. K. Rasmussen, "Generalized fusion algorithm for compressive sampling reconstruction and RIP-based analysis," Signal Processing, vol. 139, s. 36-48, 2017.
[17]
K. Li et al., "Alternating strategies with internal ADMM for low-rank matrix reconstruction," Signal Processing, vol. 121, s. 153-159, 2016.
[18]
M. Vehkaperä, Y. Kabashima och S. Chatterjee, "Analysis of Regularized LS Reconstruction and Random Matrix Ensembles in Compressed Sensing," IEEE Transactions on Information Theory, vol. 62, no. 4, s. 2100-2124, 2016.
[19]
D. Sundman, S. Chatterjee och M. Skoglund, "Design and Analysis of a Greedy Pursuit for Distributed Compressed Sensing," IEEE Transactions on Signal Processing, vol. 64, no. 11, s. 2803-2818, 2016.
[20]
M. Sundin et al., "Relevance Singular Vector Machine for Low-Rank Matrix Reconstruction," IEEE Transactions on Signal Processing, vol. 64, no. 20, s. 5327-5339, 2016.
[21]
D. Koslicki et al., "ARK : Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition," PLOS ONE, vol. 10, no. 10, 2015.
[22]
S. K. Ambat, S. Chatterjee och K. V. S. Hari, "A Committee Machine Approach for Compressed Sensing Signal Reconstruction," IEEE Transactions on Signal Processing, vol. 62, no. 7, s. 1705-1717, 2014.
[23]
Z. Ma et al., "Dirichlet mixture modeling to estimate an empirical lower bound for LSF quantization," Signal Processing, vol. 104, s. 291-295, 2014.
[24]
D. Sundman, S. Chatterjee och M. Skoglund, "Distributed greedy pursuit algorithms," Signal Processing, vol. 105, s. 298-315, 2014.
[25]
D. Zachariah et al., "Estimation for the Linear Model With Uncertain Covariance Matrices," IEEE Transactions on Signal Processing, vol. 62, no. 6, s. 1525-1535, 2014.
[26]
A. Shirazinia, S. Chatterjee och M. Skoglund, "Joint Source-Channel Vector Quantization for Compressed Sensing," IEEE Transactions on Signal Processing, vol. 62, no. 14, s. 3667-3681, 2014.
[27]
D. Sundman, C. Saikat och M. Skoglund, "Methods for Distributed Compressed Sensing," Journal of Sensor and Actuator Networks, vol. 3, no. 1, s. 1-25, 2014.
[28]
S. K. Ambat, S. Chatterjee och K. V. S. Hari, "Progressive fusion of reconstruction algorithms for low latency applications in compressed sensing," Signal Processing, vol. 97, s. 146-151, 2014.
[29]
S. Chatterjee et al., "SEK: Sparsity exploiting k-mer-based estimation of bacterial community composition," Bioinformatics, vol. 30, no. 17, s. 2423-2431, 2014.
[30]
A. Shirazinia, S. Chatterjee och M. Skoglund, "Analysis-by-Synthesis Quantization for Compressed Sensing Measurements," IEEE Transactions on Signal Processing, vol. 61, no. 22, s. 5789-5800, 2013.
[31]
S. K. Ambat, S. Chatterjee och K. V. S. Hari, "Fusion of Algorithms for Compressed Sensing," IEEE Transactions on Signal Processing, vol. 61, no. 14, s. 3699-3704, 2013.
[32]
D. Zachariah et al., "Line spectrum estimation with probabilistic priors," Signal Processing, vol. 93, no. 11, s. 2969-2974, 2013.
[33]
J. T. Flam et al., "The linear model under mixed gaussian inputs : Designing the transfer matrix," IEEE Transactions on Signal Processing, vol. 61, no. 21, s. 5247-5259, 2013.
[34]
D. Zachariah et al., "Alternating Least-Squares for Low-Rank Matrix Reconstruction," IEEE Signal Processing Letters, vol. 19, no. 4, s. 231-234, 2012.
[35]
D. Zachariah, S. Chatterjee och M. Jansson, "Dynamic Iterative Pursuit," IEEE Transactions on Signal Processing, vol. 60, no. 9, s. 4967-4972, 2012.
[36]
S. Chatterjee et al., "On MMSE estimation: A linear model under Gaussian mixture statistics," IEEE Transactions on Signal Processing, vol. 60, no. 7, s. 3840-3845, 2012.
[37]
S. Chatterjee et al., "Projection-based and look ahead strategies for atom selection," IEEE Transactions on Signal Processing, vol. 60, no. 2, s. 634-647, 2012.
[38]
Y. Kabashima, M. Vehkaperä och S. Chatterjee, "Typical l(1)-recovery limit of sparse vectors represented by concatenations of random orthogonal matrices," Journal of Statistical Mechanics : Theory and Experiment, vol. 2012, no. 12, s. P12003, 2012.
[39]
S. Chatterjee och W. B. Kleijn, "Auditory Model-Based Design and Optimization of Feature Vectors for Automatic Speech Recognition," IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 6, s. 1813-1825, 2011.
[40]
S. Chatterjee och T. Sreenivas, "Reduced complexity two stage vector quantization," Digital signal processing (Print), vol. 19, no. 3, s. 476-490, 2009.
[41]
S. Chatterjee och T. Sreenivas, "Optimum switched split vector quantization of LSF parameters," Signal Processing, vol. 88, no. 6, s. 1528-1538, 2008.
[42]
S. Chatterjee och T. Sreenivas, "Optimum transform domain split VQ," IEEE Signal Processing Letters, vol. 15, s. 285-288, 2008.
[43]
S. Chatterjee och T. Sreenivas, "Predicting VQ performance bound for LSF coding," IEEE Signal Processing Letters, vol. 15, s. 166-169, 2008.
[44]
S. Chatterjee och T. Sreenivas, "Switched conditional PDF-based split VQ using Gaussian mixture model," IEEE Signal Processing Letters, vol. 15, s. 91-94, 2008.
[45]
S. Chatterjee och T. Sreenivas, "Analysis of conditional PDF based split VQ," IEEE Signal Processing Letters, vol. 14, no. 11, s. 781-784, 2007.
[46]
S. Chatterjee och T. Sreenivas, "Conditional PDF-based split vector quantization of wideband LSF parameters," IEEE Signal Processing Letters, vol. 14, no. 9, s. 641-644, 2007.
Konferensbidrag
[47]
A. Honore, A. Ghosh och S. Chatterjee, "Compressed Sensing of Generative Sparse-Latent (GSL) Signals," i 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings, 2023, s. 1918-1922.
[48]
X. Liang et al., "DeePMOS : Deep Posterior Mean-Opinion-Score of Speech," i Interspeech 2023, 2023, s. 526-530.
[49]
F. Cumlin, C. Schüldt och S. Chatterjee, "Latent-based Neural Net for Non-intrusive Speech Quality Assessment," i 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings, 2023, s. 226-230.
[50]
S. Das et al., "M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance," i IV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings, 2023.
[51]
P. G. Jurado, X. Liang och S. Chatterjee, "Deterministic transform based weight matrices for neural networks," i 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, s. 4528-4532.
[52]
S. Das et al., "Extrinsic Calibration and Verification of Multiple Non-overlapping Field of View Lidar Sensors," i 2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022), 2022.
[53]
M. Amini et al., "Interpretable PET/CT Radiomic Based Prognosis Modeling of NSCLC Recurrent Following Complete Resection," i 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, 2022.
[54]
S. Das et al., "Neural Greedy Pursuit for Feature Selection," i 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022.
[55]
A. Ghosh et al., "Time-varying Normalizing Flow for Generative Modeling of Dynamical Signals," i 2022 30Th European Signal Processing Conference (EUSIPCO 2022), 2022, s. 1492-1496.
[56]
A. M. Javid et al., "A Relu Dense Layer To Improve The Performance Of Neural Networks," i 2021 IEEE International Conference On Acoustics, Speech And Signal Processing (ICASSP 2021), 2021, s. 2810-2814.
[57]
X. Liang et al., "Asynchronous Decentralized Learning of Randomization-based Neural Networks," i International Joint Conference on Neural Networks (IJCNN), 2021.
[58]
H. Nylén, S. Chatterjee och S. Ternström, "Detecting Signal Corruptions in Voice Recordings For Speech Therapy," i ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, s. 386-390.
[59]
X. Liang, M. Skoglund och S. Chatterjee, "Feature Reuse For A Randomization Based Neural Network," i 2021 Ieee International Conference On Acoustics, Speech And Signal Processing (ICASSP 2021), 2021, s. 2805-2809.
[60]
X. Liang et al., "Learning without Forgetting for Decentralized Neural Nets with Low Communication Overhead," i 2020 28th European Signal Processing Conference (EUSIPCO), 2021, s. 2185-2189.
[61]
P. G. Jurado et al., "Use of Deterministic Transforms to Design Weight Matrices of a Neural Network," i 29th European Signal Processing Conference (EUSIPCO 2021), 2021, s. 1366-1370.
[62]
X. Liang et al., "A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning," i 2020 International joint conference on neural networks (IJCNN), 2020.
[63]
A. M. Javid et al., "Adaptive Learning without Forgetting via Low-Complexity Convex Networks," i 28th European Signal Processing Conference (EUSIPCO 2020), 2020, s. 1623-1627.
[64]
X. Liang et al., "Asynchrounous decentralized learning of a neural network," i Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, 2020, s. 3947-3951.
[65]
F. Tsai, A. M. Javid och S. Chatterjee, "Design of a Non-negative Neural Network to Improve on NMF," i 28thEuropean Signal Processing Conference (EUSIPCO 2020), 2020, s. 461-465.
[66]
Z. Li et al., "Dual sentence representation model integrating prior knowledge for bio-text-mining," i 2020 IEEE international conference on bioinformatics and biomedicine, 2020, s. 2409-2416.
[67]
A. Venkitaraman, S. Chatterjee och P. Händel, "Gaussian Processes over Graphs," i 2020 IEEE International Conference on Acoustics Speech and Signal Processing ICASSP, 2020, s. 5640-5644.
[68]
A. Honore et al., "Hidden markov models for sepsis detection in preterm infants," i Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, 2020, s. 1130-1134.
[69]
A. M. Javid et al., "High-dimensional neural feature using rectified linear unit and random matrix instance," i 2020 IEEE international conference on acoustics, speech, and signal processing, 2020, s. 4237-4241.
[70]
D. Liu et al., "Neural Network based Explicit Mixture Models and Expectation-maximization based Learning," i Proceedings of the International Joint Conference on Neural Networks, 2020.
[71]
D. Liu et al., "Powering hidden markov model by neural network based generative models," i ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2020, s. 1324-1331.
[72]
A. Venkitaraman, S. Chatterjee och B. Wahlberg, "Recursive Prediction of Graph Signals with Incoming Nodes," i 2020 IEEE International Conference on Acoustics, Speech, And Signal Processing, 2020, s. 5565-5569.
[73]
A. Ghosh et al., "Robust classification using hidden markov models and mixtures of normalizing flows," i 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020.
[74]
A. Zaki och S. Chatterjee, "Convex optimization based sparse learning over networks," i 2019 27th European Signal Processing Conference (EUSIPCO), 2019.
[75]
D. Liu et al., "ENTROPY-REGULARIZED OPTIMAL TRANSPORT GENERATIVE MODELS," i 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2019, s. 3532-3536.
[76]
A. Venkitaraman, P. Frossard och S. Chatterjee, "KERNEL REGRESSION FOR GRAPH SIGNAL PREDICTION IN PRESENCE OF SPARSE NOISE," i 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2019, s. 5426-5430.
[77]
M. Sadeghi et al., "L0soft : ℓ0 minimization via soft thresholding," i Proceedings of the 27th European Signal Processing Conference (EUSIPCO), 2019.
[78]
D. Liu et al., "alpha Belief Propagation as Fully Factorized Approximation," i 2019 7TH IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (IEEE GLOBALSIP), 2019.
[79]
X. Liang et al., "DISTRIBUTED LARGE NEURAL NETWORK WITH CENTRALIZED EQUIVALENCE," i 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, s. 2976-2980.
[80]
A. Venkitaraman, S. Chatterjee och P. Händel, "Extreme learning machine for graph signal processing," i 2018 26th European Signal Processing Conference (EUSIPCO), 2018, s. 136-140.
[81]
A. Venkitaraman, S. Chatterjee och P. Händel, "MULTI-KERNEL REGRESSION FOR GRAPH SIGNAL PROCESSING," i 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, s. 4644-4648.
[82]
A. M. Javid, S. Chatterjee och M. Skoglund, "Mutual Information Preserving Analysis of a Single Layer Feedforward Network," i Proceedings of the International Symposium on Wireless Communication Systems, 2018.
[83]
M. Sundin et al., "A Connectedness Constraint for Learning Sparse Graphs," i 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2017, s. 151-155.
[84]
A. Zaki et al., "Distributed Greedy Sparse Learning over Doubly Stochastic Networks," i 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2017, s. 361-364.
[85]
G. Fotedar et al., "Automatic recognition of social roles using long term role transitions in small group interactions," i Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2016, s. 2065-2069.
[86]
M. Sundin, S. Chatterjee och M. Jansson, "Bayesian Cramer-Rao bounds for factorized model based low rank matrix reconstruction," i 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2016, s. 1227-1231.
[87]
M. Sundin, S. Chatterjee och M. Jansson, "Bayesian learning for robust principal component analysis," i 2015 23rd European Signal Processing Conference, EUSIPCO 2015, 2015, s. 2361-2365.
[88]
A. Venkitaraman, S. Chatterjee och P. Händel, "Graph linear prediction results in smaller error than standard linear prediction," i 2015 23rd European Signal Processing Conference, EUSIPCO 2015, 2015, s. 220-224.
[89]
M. Sundin, S. Chatterjee och M. Jansson, "Greedy minimization of l1-norm with high empirical success," i 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015, 2015.
[90]
A. Zaki, S. Chatterjee och L. K. Rasmussen, "Universal algorithm for compressive sampling," i 2015 23rd European Signal Processing Conference, EUSIPCO 2015, 2015, s. 689-693.
[91]
C. Koniaris och S. Chatterjee, "A sparsity based preprocessing for noise robust speech recognition," i 2014 IEEE Workshop on Spoken Language Technology, SLT 2014 - Proceedings, 2014, s. 513-518.
[92]
M. Vehkapera, Y. Kabashima och S. Chatterjee, "Analysis of regularized LS reconstruction and random matrix ensembles in compressed sensing," i 2014 IEEE International Symposium on Information Theory, ISIT 2014, 29 June 2014 through 4 July 2014, Honolulu, HI, 2014, s. 3185-3189.
[93]
M. Sundin, S. Chatterjee och M. Jansson, "COMBINED MODELING OF SPARSE AND DENSE NOISE IMPROVES BAYESIAN RVM," i 2014 PROCEEDINGS OF THE 22ND EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2014, s. 1841-1845.
[94]
M. Sundin, S. Chatterjee och M. Jansson, "Combined Modelling of Sparse and Dense noise improves Bayesian RVM," i Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), 2014, 2014, s. 1841-1845.
[95]
A. Shirazinia, S. Chatterjee och M. Skoglund, "Distributed Quantization for Compressed Sensing," i 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014; Florence; Italy; 4 May 2014 through 9 May 2014, 2014, s. 6439-6443.
[96]
K. Li et al., "Piecewise Toeplitz matrices-based sensing for rank minimization," i European Signal Processing Conference, 2014, s. 1836-1840.
[97]
P. B. Swamy et al., "Reduced look ahead orthogonal matching pursuit," i 2014 20th National Conference on Communications, NCC 2014, 2014, s. 6811329.
[98]
M. Sundin et al., "Relevance Singular Vector Machine for low rank matrix sensing," i Signal Processing and Communications (SPCOM), 2014 International Conference on, 2014, s. 1-5.
[99]
A. Shirazinia, S. Chatterjee och M. Skoglund, "Analysis-by-synthesis-based Quantization of Compressed Sensing Measurements," i 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2013, 2013, s. 5810-5814.
[100]
A. Shirazinia, S. Chatterjee och M. Skoglund, "Channel-optimized Vector Quantizer Design for Compressed Sensing Measurements," i 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2013, 2013, s. 4648-4652.
[101]
M. Sundin, M. Jansson och S. Chatterjee, "Conditional prior based lmmse estimation of sparse signals," i 2013 Proceedings of the 21st European Signal Processing Conference (EUSIPCO), 2013, s. 6811629.
[102]
D. Sundman et al., "Distributed Predictive Subspace Pursuit," i 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2013, 2013, s. 4633-4637.
[103]
D. Zachariah, M. Jansson och S. Chatterjee, "Enhanced capon beamformer using regularized covariance matching," i 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013, s. 97-100.
[104]
S. K. Ambat, S. Chatterjee och K. V. S. Hari, "Fusion of algorithms for Compressed Sensing," i ICASSP IEEE Int Conf Acoust Speech Signal Process Proc, 2013, s. 5860-5864.
[105]
D. Zachariah, S. Chatterjee och M. Jansson, "Iteratively Reweighted Least Squares for Reconstruction of Low-Rank Matrices with Linear Structure," i 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013, s. 6456-6460.
[106]
D. Sundman, S. Chatterjee och M. Skoglund, "Parallel pursuit for distributed compressed sensing," i 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 2013, s. 783-786.
[107]
J. Flåm, E. Björnson och S. Chatterjee, "Pilot design for MIMO channel estimation : An alternative to the Kronecker structure assumption," i ICASSP IEEE Int Conf Acoust Speech Signal Process Proc, 2013, s. 5061-5064.
[108]
M. Vehkaperä, Y. Kabashima och S. Chatterjee, "Statistical mechanics approach to sparse noise denoising," i 2013 Proceedings of the 21st European Signal Processing Conference (EUSIPCO), 2013, s. 6811435.
[109]
D. Sundman, C. Saikat och M. Skoglund, "A Greedy Pursuit Algorithm for Distributed Compressed Sensing," i Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 2012, s. 2729-2732.
[110]
S. K. Ambat, S. Chatterjee och K. V. S. Hari, "Adaptive selection of search space in look ahead orthogonal matching pursuit," i 2012 National Conference on Communications, NCC 2012, 2012, s. 6176852.
[111]
M. Vehkaperä et al., "Analysis of Sparse Representations Using Bi-Orthogonal Dictionaries," i Information Theory Workshop (ITW), 2012 IEEE, 2012, s. 647-651.
[112]
B. S. Mysore Rama Rao, S. Chatterjee och B. Ottersten, "Detection of sparse random signals using compressive measurements," i Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 2012, s. 3257-3260.
[113]
D. Zachariah, C. Saikat och M. Jansson, "Dynamic subspace pursuit," i Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 2012, s. 3605-3608.
[114]
D. Sundman, S. Chatterjee och M. Skoglund, "FROGS : A serial reversible greedy search algorithm," i 2012 Swedish Communication Technologies Workshop, Swe-CTW 2012, 2012, s. 40-45.
[115]
S. K. Ambat, S. Chatterjee och K. Hari, "Fusion of greedy pursuits for compressed sensing signal reconstruction," i 2012 Proceedings Of The 20th European Signal Processing Conference (EUSIPCO), 2012, s. 1434-1438.
[116]
S. K. Ambat, S. Chatterjee och K. V. S. Hari, "On selection of search space dimension in compressive sampling matching pursuit," i TENCON 2012 - 2012 IEEE Region 10 Conference, 2012, s. 6412345.
[117]
A. Shirazinia, S. Chatterjee och M. Skoglund, "Performance Bounds for Vector Quantized Compressive Sensing," i 2012 International Symposium on Information Theory and Its Applications, ISITA 2012, 2012, s. 289-293.
[118]
S. Chatterjee et al., "Projection-based atom selection in orthogonal matching pursuit for compressive sensing," i 2012 National Conference on Communications, NCC 2012, 2012, s. 6176797.
[119]
S. K. Ambat, S. Chatterjee och K. V. S. Hari, "Subspace pursuit embedded in orthogonal matching pursuit," i TENCON 2012 - 2012 IEEE Region 10 Conference, 2012, s. 6412325.
[120]
M. Vehkaperä, S. Chatterjee och M. Skoglund, "Analysis of MMSE estimation for compressive sensing of block sparse signals," i 2011 IEEE Information Theory Workshop, ITW 2011, 2011, s. 553-557.
[121]
J. Flåm, J. Jaldén och S. Chatterjee, "Gaussian mixture modeling for source localization," i ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011, s. 2604-2607.
[122]
D. Sundman, S. Chatterjee och M. Skoglund, "Greedy pursuits for compressed sensing of jointly sparse signals," i European Signal Processing Conference, 2011, s. 368-372.
[123]
D. Sundman, S. Chatterjee och M. Skolglund, "Greedy pursuits of compressed sensing of jointly sparse signal," i The 2011 European Signal Processing Conference (EUSIPCO‐2011). Barcelona, Spain. August 29- September 2, 2011, 2011.
[124]
S. Chatterjee, D. Sundman och M. Skoglund, "Hybrid greedy pursuit," i 19th European Signal Processing Conference (EUSIPCO 2011), 2011, s. 343-347.
[125]
D. Sundman, C. Saikat och M. Skoglund, "Look Ahead Parallel Pursuit," i 2011 IEEE Swedish Communication Technologies Workshop, Swe-CTW 2011, 2011, s. 114-117.
[126]
S. Chatterjee, D. Sundman och M. Skoglund, "Look ahead orthogonal matching pursuit," i ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011, s. 4024-4027.
[127]
S. Chatterjee, D. Sundman och M. Skolglund, "Robust matching pursuit for recovery of Gaussian sparse signal," i 2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings, 2011, s. 420-424.
[128]
S. Chatterjee och W. B. Kleijn, "AUDITORY MODEL BASED MODIFIED MFCC FEATURES," i 2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, s. 4590-4593.
[129]
D. Sundman, S. Chatterjee och M. Skoglund, "On the use of Compressive Sampling for Wide-band Spectrum Sensing," i 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2010, s. 354-359.
[130]
C. Koniaris, S. Chatterjee och W. B. Kleijn, "Selecting static and dynamic features using an advanced auditory model for speech recognition," i Proceedings 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2010, s. 4590-4593.
[131]
S. Chatterjee, D. Sundman och M. Skolglund, "Statistical post-processing improves basis pursuit denoising performance," i 2010 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2010, 2010, s. 23-27.
[132]
S. Chatterjee och M. Skoglund, "Structured Gaussian Mixture model based product VQ," i 18th European Signal Processing Conference (EUSIPCO-2010), 2010, s. 771-775.
[133]
S. Chatterjee och T. Sreenivas, "Analysis-by-synthesis based switched transform domain split VQ using Gaussian mixture model," i 2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS, 2009, s. 4117-4120.
[134]
S. Chatterjee, C. Koniaris och W. B. Kleijn, "Auditory model based optimization of MFCCs improves automatic speech recognition performance," i INTERSPEECH 2009 : 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, 2009, s. 2943-2946.
[135]
A. Kundu, S. Chatterjee och T. Sreenivas, "GMM based Bayesian approach to speech enhancement in signal/transform domain," i 2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 2008, s. 4893-4896.
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