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