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  • 1. Ambat, S. K.
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hari, K. V. S.
    Adaptive selection of search space in look ahead orthogonal matching pursuit2012In: 2012 National Conference on Communications, NCC 2012, 2012, p. 6176852-Conference paper (Refereed)
    Abstract [en]

    Compressive Sensing theory combines the signal sampling and compression for sparse signals resulting in reduction in sampling rate and computational complexity of the measurement system. In recent years, many recovery algorithms were proposed to reconstruct the signal efficiently. Look Ahead OMP (LAOMP) is a recently proposed method which uses a look ahead strategy and performs significantly better than other greedy methods. In this paper, we propose a modification to the LAOMP algorithm to choose the look ahead parameter L adaptively, thus reducing the complexity of the algorithm, without compromising on the performance. The performance of the algorithm is evaluated through Monte Carlo simulations.

  • 2. Ambat, S. K.
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hari, K. V. S.
    Fusion of algorithms for Compressed Sensing2013In: ICASSP IEEE Int Conf Acoust Speech Signal Process Proc, 2013, p. 5860-5864Conference paper (Refereed)
    Abstract [en]

    Numerous algorithms have been proposed recently for sparse signal recovery in Compressed Sensing (CS). In practice, the number of measurements can be very limited due to the nature of the problem and/or the underlying statistical distribution of the non-zero elements of the sparse signal may not be known a priori. It has been observed that the performance of any sparse signal recovery algorithm depends on these factors, which makes the selection of a suitable sparse recovery algorithm difficult. To take advantage in such situations, we propose to use a fusion framework using which we employ multiple sparse signal recovery algorithms and fuse their estimates to get a better estimate. Theoretical results justifying the performance improvement are shown. The efficacy of the proposed scheme is demonstrated by Monte Carlo simulations using synthetic sparse signals and ECG signals selected from MIT-BIH database.

  • 3. Ambat, S. K.
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hari, K. V. S.
    On selection of search space dimension in compressive sampling matching pursuit2012In: TENCON 2012 - 2012 IEEE Region 10 Conference, IEEE , 2012, p. 6412345-Conference paper (Refereed)
    Abstract [en]

    Compressive Sampling Matching Pursuit (CoSaMP) is one of the popular greedy methods in the emerging field of Compressed Sensing (CS). In addition to the appealing empirical performance, CoSaMP has also splendid theoretical guarantees for convergence. In this paper, we propose a modification in CoSaMP to adaptively choose the dimension of search space in each iteration, using a threshold based approach. Using Monte Carlo simulations, we show that this modification improves the reconstruction capability of the CoSaMP algorithm in clean as well as noisy measurement cases. From empirical observations, we also propose an optimum value for the threshold to use in applications.

  • 4. Ambat, S. K.
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hari, K. V. S.
    Subspace pursuit embedded in orthogonal matching pursuit2012In: TENCON 2012 - 2012 IEEE Region 10 Conference, IEEE , 2012, p. 6412325-Conference paper (Refereed)
    Abstract [en]

    Orthogonal Matching Pursuit (OMP) is a popular greedy pursuit algorithm widely used for sparse signal recovery from an undersampled measurement system. However, one of the main shortcomings of OMP is its irreversible selection procedure of columns of measurement matrix. i.e., OMP does not allow removal of the columns wrongly estimated in any of the previous iterations. In this paper, we propose a modification in OMP, using the well known Subspace Pursuit (SP), to refine the subspace estimated by OMP at any iteration and hence boost the sparse signal recovery performance of OMP. Using simulations we show that the proposed scheme improves the performance of OMP in clean and noisy measurement cases.

  • 5. Ambat, Sooraj K.
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hari, K. V. S.
    A Committee Machine Approach for Compressed Sensing Signal Reconstruction2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 7, p. 1705-1717Article in journal (Refereed)
    Abstract [en]

    Although many sparse recovery algorithms have been proposed recently in compressed sensing (CS), it is well known that the performance of any sparse recovery algorithm depends on many parameters like dimension of the sparse signal, level of sparsity, and measurement noise power. It has been observed that a satisfactory performance of the sparse recovery algorithms requires a minimum number of measurements. This minimum number is different for different algorithms. In many applications, the number of measurements is unlikely to meet this requirement and any scheme to improve performance with fewer measurements is of significant interest in CS. Empirically, it has also been observed that the performance of the sparse recovery algorithms also depends on the underlying statistical distribution of the nonzero elements of the signal, which may not be known a priori in practice. Interestingly, it can be observed that the performance degradation of the sparse recovery algorithms in these cases does not always imply a complete failure. In this paper, we study this scenario and show that by fusing the estimates of multiple sparse recovery algorithms, which work with different principles, we can improve the sparse signal recovery. We present the theoretical analysis to derive sufficient conditions for performance improvement of the proposed schemes. We demonstrate the advantage of the proposed methods through numerical simulations for both synthetic and real signals.

  • 6. Ambat, Sooraj K.
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hari, K. V. S.
    Fusion of Algorithms for Compressed Sensing2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 14, p. 3699-3704Article in journal (Refereed)
    Abstract [en]

    For compressed sensing (CS), we develop a new scheme inspired by data fusion principles. In the proposed fusion based scheme, several CS reconstruction algorithms participate and they are executed in parallel, independently. The final estimate of the underlying sparse signal is derived by fusing the estimates obtained from the participating algorithms. We theoretically analyze this fusion based scheme and derive sufficient conditions for achieving a better reconstruction performance than any participating algorithm. Through simulations, we show that the proposed scheme has two specific advantages: 1) it provides good performance in a low dimensional measurement regime, and 2) it can deal with different statistical natures of the underlying sparse signals. The experimental results on real ECG signals shows that the proposed scheme demands fewer CS measurements for an approximate sparse signal reconstruction.

  • 7. Ambat, Sooraj K.
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hari, K. V. S.
    Progressive fusion of reconstruction algorithms for low latency applications in compressed sensing2014In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 97, p. 146-151Article in journal (Refereed)
    Abstract [en]

    Recently, it has been shown that fusion of the estimates of a set of sparse recovery algorithms result in an estimate better than the best estimate in the set, especially when the number of measurements is very limited. Though these schemes provide better sparse signal recovery performance, the higher computational requirement makes it less attractive for low latency applications. To alleviate this drawback, in this paper, we develop a progressive fusion based scheme for low latency applications in compressed sensing. In progressive fusion, the estimates of the participating algorithms are fused progressively according to the availability of estimates. The availability of estimates depends on computational complexity of the participating algorithms, in turn on their latency requirement. Unlike the other fusion algorithms, the proposed progressive fusion algorithm provides quick interim results and successive refinements during the fusion process, which is highly desirable in low latency applications. We analyse the developed scheme by providing sufficient conditions for improvement of CS reconstruction quality and show the practical efficacy by numerical experiments using synthetic and real-world data.

  • 8.
    Ambat, Sooraj K.
    et al.
    IISc - Indian Institute of Science.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hari, K.V.S.
    IISc - Indian Institute of Science.
    Fusion of greedy pursuits for compressed sensing signal reconstruction2012In: 2012 Proceedings Of The 20th European Signal Processing Conference (EUSIPCO), IEEE Computer Society, 2012, p. 1434-1438Conference paper (Refereed)
    Abstract [en]

    Greedy Pursuits are very popular in Compressed Sensing for sparse signal recovery. Though many of the Greedy Pursuits possess elegant theoretical guarantees for performance, it is well known that their performance depends on the statistical distribution of the non-zero elements in the sparse signal. Inpractice, the distribution of the sparse signal may not be knowna priori. It is also observed that performance of Greedy Pursuits degrades as the number of available measurements decreases from a threshold value which is method dependent. To improve the performance in these situations, we introduce a novel fusion framework for Greedy Pursuits and also propose two algorithms for sparse recovery. Through Monte Carlo simulations we show that the proposed schemes improve sparse signal recovery in clean as well as noisy measurement cases.

  • 9.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Flåm, John T.
    NTNU - Norwegian University of Science and Technology.
    Kansanen, Kimmo
    NTNU - Norwegian University of Science and Technology.
    Ekman, Tobjorn
    NTNU - Norwegian University of Science and Technology.
    On MMSE estimation: A linear model under Gaussian mixture statistics2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 7, p. 3840-3845Article in journal (Refereed)
    Abstract [en]

    In a Bayesian linear model, suppose observation y = Hx + n stems from independent inputs x and n which are Gaussian mixture (GM) distributed. With known matrix H, the minimum mean square error (MMSE) estimator for x , has analytical form. However, its performance measure, the MMSE itself, has no such closed form. Because existing Bayesian MMSE bounds prove to have limited practical value under these settings, we instead seek analytical bounds for the MMSE, both upper and lower. This paper provides such bounds, and relates them to the signal-to-noise-ratio (SNR).

  • 10.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hari, K. V. S.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Projection-based atom selection in orthogonal matching pursuit for compressive sensing2012In: 2012 National Conference on Communications, NCC 2012, IEEE , 2012, p. 6176797-Conference paper (Refereed)
    Abstract [en]

    For compressive sensing, we endeavor to improve the atom selection strategy of the existing orthogonal matching pursuit (OMP) algorithm. To achieve a better estimate of the underlying support set progressively through iterations, we use a least squares solution based atom selection method. From a set of promising atoms, the choice of an atom is performed through a new method that uses orthogonal projection along-with a standard matched filter. Through experimental evaluations, the effect of projection based atom selection strategy is shown to provide a significant improvement for the support set recovery performance, in turn, the compressive sensing recovery.

  • 11.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Kleijn, W. Bastiaan
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    AUDITORY MODEL BASED MODIFIED MFCC FEATURES2010In: 2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, p. 4590-4593Conference paper (Refereed)
    Abstract [en]

    Using spectral and spectro-temporal auditory models, we develop a computationally simple feature vector based on the design architecture of existing mel frequency cepstral coefficients (MFCCs). Along with the use of an optimized static function to compress a set of filter bank energies, we propose to use a memory-based adaptive compression function to incorporate the behavior of human auditory response across time and frequency. We show that a significant improvement in automatic speech recognition (ASR) performance is obtained for any environmental condition, clean as well as noisy.

  • 12.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Kleijn, W. Bastiaan
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Auditory Model-Based Design and Optimization of Feature Vectors for Automatic Speech Recognition2011In: IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, E-ISSN 1558-7924, Vol. 19, no 6, p. 1813-1825Article in journal (Refereed)
    Abstract [en]

    Using spectral and spectro-temporal auditory models along with perturbation-based analysis, we develop a new framework to optimize a feature vector such that it emulates the behavior of the human auditory system. The optimization is carried out in an offline manner based on the conjecture that the local geometries of the feature vector domain and the perceptual auditory domain should be similar. Using this principle along with a static spectral auditory model, we modify and optimize the static spectral mel frequency cepstral coefficients (MFCCs) without considering any feedback from the speech recognition system. We then extend the work to include spectro-temporal auditory properties into designing a new dynamic spectro-temporal feature vector. Using a spectro-temporal auditory model, we design and optimize the dynamic feature vector to incorporate the behavior of human auditory response across time and frequency. We show that a significant improvement in automatic speech recognition (ASR) performance is obtained for any environmental condition, clean as well as noisy.

  • 13.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Koniaris, Christos
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Kleijn, W. Baastian
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Auditory model based optimization of MFCCs improves automatic speech recognition performance2009In: INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, 2009, p. 2943-2946Conference paper (Refereed)
    Abstract [en]

    Using a spectral auditory model along with perturbation based analysis, we develop a new framework to optimize a set of features such that it emulates the behavior of the human auditory system. The optimization is carried out in an off-line manner based on the conjecture that the local geometries of the feature domain and the perceptual auditory domain should be similar. Using this principle, we modify and optimize the static mel frequency cepstral coefficients (MFCCs) without considering any feedback from the speech recognition system. We show that improved recognition performance is obtained for any environmental condition, clean as well as noisy.

  • 14.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Koslicki, David
    Dept of Mathematics, Oregon State University, Corvallis, USA.
    Dong, Siyuan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Innocenti, Nicolas
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Cheng, Lu
    Dept of Mathematics and Statistics, University of Helsinki, Finland.
    Lan, Yueheng
    Dept of Physics, Tsinghua University, Beijing, China.
    Vehkaperä, Mikko
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Communication Theory.
    K. Rasmussen, Lars
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Aurell, Erik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Corander, Jukka
    Dept of Signal Processing, Aalto University, Finland.
    SEK: Sparsity exploiting k-mer-based estimation of bacterial community composition2014In: Bioinformatics, ISSN 1460-2059, Vol. 30, no 17, p. 2423-2431Article in journal (Refereed)
    Abstract [en]

    Motivation: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. As the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically time-consuming in a desktop computing environment.

    Results: Using sparsity enforcing methods from the general sparse signal processing field (such as compressed sensing), we derive a solution to the community composition estimation problem by a simultaneous assignment of all sample reads to a pre-processed reference database. A general statistical model based on kernel density estimation techniques is introduced for the assignment task, and the model solution is obtained using convex optimization tools. Further, we design a greedy algorithm solution for a fast solution. Our approach offers a reasonably fast community composition estimation method, which is shown to be more robust to input data variation than a recently introduced related method.

    Availability and implementation: A platform-independent Matlab implementation of the method is freely available at http://www.ee.kth.se/ctsoftware; source code that does not require access to Matlab is currently being tested and will be made available later through the above Web site.

  • 15.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Structured Gaussian Mixture model based product VQ2010In: 18th European Signal Processing Conference (EUSIPCO-2010), EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP , 2010, p. 771-775Conference paper (Refereed)
    Abstract [en]

    In this paper, the Gaussian mixture model (GMM) based parametric framework is used to design a product vector quantization (PVQ) method that provides rate-distortion (R/D) performance optimality and bitrate scalability. We use a GMM consisting of a large number of Gaussian mixtures and invoke a block isotropic structure on the covariance matrices of the Gaussian mixtures. Using such a structured GMM, we design an optimum and bitrate scalable PVQ, namely an split (SVQ), for each Gaussian mixture. The use of an SVQ allows for a trade-off between complexity and R/D performance that spans the two extreme limits provided by an optimum scalar quantizer and an unconstrained vector quantizer. The efficacy of the new GMM based PVQ (GMPVQ) method is demonstrated for the application of speech spectrum quantization.

  • 16.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    A mixed-split scheme for 2D-DPCM based LSF quantization2005In: TENCON 2005 - 2005 IEEE REGION 10 CONFERENCE, VOLS 1-5, 2005, p. 864-869Conference paper (Refereed)
  • 17.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Analysis of conditional PDF based split VQ2007In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 14, no 11, p. 781-784Article in journal (Refereed)
  • 18.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Analysis-by-synthesis based switched transform domain split VQ using Gaussian mixture model2009In: 2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS, 2009, p. 4117-4120Conference paper (Refereed)
  • 19.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Comparison of prediction based LSF quantization methods using split VQ2006In: INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, VOLS 1-5, 2006, p. 237-240Conference paper (Refereed)
  • 20.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Computationally efficient optimum weighting function for vector quantization of LSF parameters2007In: 2007 9TH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOLS 1-3  Pages: 732-735, 2007Conference paper (Refereed)
  • 21.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Conditional PDF-based split vector quantization of wideband LSF parameters2007In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 14, no 9, p. 641-644Article in journal (Refereed)
  • 22.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Gaussian mixture model based switched split vector quantization of LSF parameters2007In: 2007 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, VOLS 1-3, 2007, p. 704-709Conference paper (Refereed)
  • 23.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Joint inter-frame and intra-frame predictive coding of LSF parameters2007In: 2007 9TH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOLS 1-3, 2007Conference paper (Refereed)
    Abstract [en]

    We explore the performance of two dimensional (2-D) prediction based LSF quantization method for both wide-band and telephone-band (narrow-band) speech. The 2-D prediction based method exploits both the inter-frame and intra-frame correlations of LSF parameters. We show that a 4th order 2-D predictor provides optimum prediction gain as well as improved quantization performance at various choices of frame shift for both wide-band and telephone-band speech. Existing one dimensional (1-D) predictive method, exploiting only inter-frame correlation, results in poor performance at larger frame shifts; whereas proposed 2-D predictor provides lower spectral distortion as well as lower number of outliers compared to existing memory-based and memory-less methods.

  • 24.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Low complexity wide-band LSF quantization using GMM of uncorrelated Gaussian Mixtures2008Conference paper (Refereed)
  • 25.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Normalized two stage SVQ for minimum complexity wide-band LSF quantization2007In: INTERSPEECH 2007: 8TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION, VOLS 1-4, 2007, p. 261-264Conference paper (Refereed)
  • 26.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Optimum switched split vector quantization of LSF parameters2008In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 88, no 6, p. 1528-1538Article in journal (Refereed)
  • 27.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Optimum transform domain split VQ2008In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 15, p. 285-288Article in journal (Refereed)
  • 28.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Predicting VQ performance bound for LSF coding2008In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 15, p. 166-169Article in journal (Refereed)
  • 29.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Reduced complexity two stage vector quantization2009In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 19, no 3, p. 476-490Article in journal (Refereed)
  • 30.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Sequential split vector quantization of LSF parameters using conditional PDF2007In: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol IV, Pts 1-3, 2007, p. 1101-1104Conference paper (Refereed)
  • 31.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Switched conditional PDF-based split VQ using Gaussian mixture model2008In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 15, p. 91-94Article in journal (Refereed)
  • 32.
    Chatterjee, Saikat
    et al.
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Two stage transform vector quantization of LSFs for wideband speech coding2006In: INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, VOLS 1-5, 2006, p. 233-236Conference paper (Refereed)
  • 33.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Hybrid greedy pursuit2011In: 19th European Signal Processing Conference (EUSIPCO 2011), 2011, p. 343-347Conference paper (Refereed)
    Abstract [en]

    For constructing the support set of a sparse vector in the standardcompressive sensing framework, we develop a hybridgreedy pursuit algorithm that combines the advantages ofserial and parallel atom selection strategies. In an iterativeframework, the hybrid algorithm uses a joint sparsity informationextracted from the independent use of serial and parallelgreedy pursuit algorithms. Through experimental evaluations,the hybrid algorithm is shown to provide a significantimprovement for the support set recovery performance.

  • 34.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Look ahead orthogonal matching pursuit2011In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011, p. 4024-4027Conference paper (Refereed)
  • 35.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skolglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Robust matching pursuit for recovery of Gaussian sparse signal2011In: 2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings, 2011, p. 420-424Conference paper (Refereed)
    Abstract [en]

    For compressive sensing (CS) recovery of Gaussian sparse signal, we explore the framework of Bayesian linear models to achieve a robust reconstruction performance in the presence of measurement noise. Using a priori statistical knowledge, we develop a minimum mean square error (MMSE) estimation based iterative greedy search algorithm. Through experimental evaluations, we show that the new algorithm provides a robust CS reconstruction performance compared to an existing least square based algorithm.

  • 36.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skolglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Statistical post-processing improves basis pursuit denoising performance2010In: 2010 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2010, 2010, p. 23-27Conference paper (Refereed)
  • 37.
    Chatterjee, Saikat
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Sundman, Dennis
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Vehkaperä, Mikko
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Skolglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Projection-based and look ahead strategies for atom selection2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 2, p. 634-647Article in journal (Refereed)
    Abstract [en]

    In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look-ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look-ahead strategy requires a higher computational resource. To achieve a tradeoff between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms.

  • 38. Flam, J. T.
    et al.
    Zachariah, Dave
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Vehkaperä, mikko
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    The linear model under mixed gaussian inputs: Designing the transfer matrix2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 21, p. 5247-5259Article in journal (Refereed)
    Abstract [en]

    Suppose a linear model Y = Hx+n, where inputs x, n are independent Gaussian mixtures. The problem is to design the transfer matrix so as to minimize the mean square error (MSE) when estimating x from . This problem has important applications, but faces at least three hurdles. Firstly, even for a fixed H, the minimum MSE (MMSE) has no analytical form. Secondly, theMMSE is generally not convex in . Thirdly, derivatives of the MMSEw.r.t. are hard to obtain. This paper casts theproblemas a stochastic program and invokes gradient methods. The study is motivated by two applications in signal processing. One concerns the choice of error-reducing precoders; the other deals with selection of pilot matrices for channel estimation. In either setting, our numerical results indicate improved estimation accuracy-markedly better than those obtained by optimal design based on standard linear estimators. Some implications of the non-convexities of the MMSE are noteworthy, yet, to our knowledge, not well known. For example, there are cases in which more pilot power is detrimental for channel estimation. This paper explains why.

  • 39. Flåm, John
    et al.
    Björnson, Emil
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Pilot design for MIMO channel estimation: An alternative to the Kronecker structure assumption2013In: ICASSP IEEE Int Conf Acoust Speech Signal Process Proc, IEEE conference proceedings, 2013, p. 5061-5064Conference paper (Refereed)
    Abstract [en]

    This work seeks to design a pilot signal, under a power constraint, such that the channel can be estimated with minimum mean square error. The procedure we derive does not assume Kronecker structure on the underlying covariance matrices, and the pilot signal is obtained in three main steps. Firstly, we solve a relaxed convex version of the original minimization problem. Secondly, its solution is projected onto the feasible set. Thirdly we use the projected solution as starting point for an augmented Lagrangian method. Numerical experiments indicate that this procedure may produce pilot signals that are far better than those obtained under the Kronecker structure assumption.

  • 40. Flåm, John
    et al.
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Gaussian mixture modeling for source localization2011In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011, p. 2604-2607Conference paper (Refereed)
    Abstract [en]

    Exploiting prior knowledge, we use Bayesian estimation to localize a source heard by a fixed sensor network. The method has two main aspects: Firstly, the probability density function (PDF) of a function of the source location is approximated by a Gaussian mixture model (GMM). This approximation can theoretically be made arbitrarily accurate, and allows a closed form minimum mean square error (MMSE) estimator for that function. Secondly, the source location is retrieved by minimizing the Euclidean distance between the function and its MMSE estimate using a gradient method. Our method avoids the issues of a numerical MMSE estimator but shows comparable accuracy.

  • 41. Fotedar, G.
    et al.
    Aditya Gaonkar, P.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Ghosh, P. K.
    Automatic recognition of social roles using long term role transitions in small group interactions2016In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2016, p. 2065-2069Conference paper (Refereed)
    Abstract [en]

    Recognition of social roles in small group interactions is challenging because of the presence of disfluency in speech, frequent overlaps between speakers, short speaker turns and the need for reliable data annotation. In this work, we consider the problem of recognizing four roles, namely Gatekeeper, Protagonist, Neutral, and Supporter in small group interactions in AMI corpus. In general, Gatekeeper and Protagonist roles occur less frequently compared to Neutral, and Supporter. In this work, we exploit role transitions across segments in a meeting by incorporating role transition probabilities and formulating the role recognition as a decoding problem over the sequence of segments in an interaction. Experiments are performed in a five fold cross validation setup using acoustic, lexical and structural features with precision, recall and F-score as the performance metrics. The results reveal that precision averaged across all folds and different feature combinations improves in the case of Gatekeeper and Protagonist by 13.64% and 12.75% when the role transition information is used which in turn improves the F-score for Gatekeeper by 6.58% while the F-scores for the rest of the roles do not change significantly.

  • 42. Ghayem, Fateme
    et al.
    Sadeghi, Mostafa
    Babaie-Zadeh, Massoud
    Chatterjee, Saikat
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Jutten, Christian
    Sparse Signal Recovery Using Iterative Proximal Projection2018In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 4, p. 879-894Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify that the proposed algorithm considerably outperforms some well-known and recently proposed algorithms.

  • 43.
    Javid, Alireza M.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Mutual Information Preserving Analysis of a Single Layer Feedforward Network2018In: Proceedings of the International Symposium on Wireless Communication Systems, VDE Verlag GmbH , 2018Conference paper (Refereed)
    Abstract [en]

    We construct a single layer feed forward network and analyze the constructed system using information theoretic tools, such as mutual information and data processing inequality. We derive a threshold on the number of hidden nodes required to achieve a good classification performance. Classification performance is expected to saturate as we increase the number of hidden nodes more than the threshold. The threshold is further verified by experimental studies on benchmark datasets. Index Terms-Neural networks, mutual information, extreme learning machine, invertible function.

  • 44.
    Kabashima, Yoshiyuki
    et al.
    Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology.
    Vehkaperä, Mikko
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Typical l(1)-recovery limit of sparse vectors represented by concatenations of random orthogonal matrices2012In: Journal of Statistical Mechanics: Theory and Experiment, ISSN 1742-5468, E-ISSN 1742-5468, Vol. 2012, no 12, p. P12003-Article in journal (Refereed)
    Abstract [en]

    We consider the problem of recovering an N-dimensional sparse vector x from its linear transformation y = Dx of M (<N) dimensions. Minimization of the l(1)-norm of x under the constraint y = Dx is a standard approach for the recovery problem, and earlier studies report that the critical condition for typically successful l(1)-recovery is universal over a variety of randomly constructed matrices D. To examine the extent of the universality, we focus on the case in which D is provided by concatenating T = N/M matrices O-1, O-2,O- ... ,O-T drawn uniformly according to the Haar measure on the M x M orthogonal matrices. By using the replica method in conjunction with the development of an integral formula to handle the random orthogonal matrices, we show that the concatenated matrices can result in better recovery performance than that predicted by the universality when the density of non-zero signals is not uniform among the T matrix modules. The universal condition is reproduced for the special case of uniform non-zero signal densities. Extensive numerical experiments support the theoretical predictions.

  • 45. Koniaris, C.
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A sparsity based preprocessing for noise robust speech recognition2014In: 2014 IEEE Workshop on Spoken Language Technology, SLT 2014 - Proceedings, 2014, p. 513-518Conference paper (Refereed)
    Abstract [en]

    We show a method to sparsify the speech input that improves the robustness of an automatic speech recognizer. The proposed scheme is added to the system as a preprocessing module prior to the acoustic feature extraction. The preprocessing module passes the input speech signal through a linear predictive (LP) analysis filter and enforces sparsity in the LP residue domain. The sparsified prediction residue finally is filtered to generate the speech signal for computing a sequence of conventional feature vectors used in automatic speech recognition (ASR). Using standard feature vectors, our experiments show that sparsification in LP residue domain improves robustness in ASR performance.

  • 46.
    Koniaris, Christos
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Kleijn, W. Baastian
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Selecting static and dynamic features using an advanced auditory model for speech recognition2010In: Proceedings 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE , 2010, p. 4590-4593Conference paper (Refereed)
    Abstract [en]

    We describe a method to select features for speech recognition that is based on a quantitative model of the human auditory periphery. The method maximizes the similarity of the geometry of the space spanned by the subset of features and the geometry of the space spanned by the auditory model output. The selection method uses a spectro-temporal auditory model that captures both frequency- and time-domain masking. The selection method is blind to the meaning of speech and does not require annotated speech data. We apply the method to the selection of a subset of features from a conventional set consisting of mel cepstra and their first-order and second-order time derivatives. Although our method uses only knowledge of the human auditory periphery, the experimental results show that it performs significantly better than feature-reduction algorithms based on linear and heteroscedastic discriminant analysis that require training with annotated speech data.

  • 47. Koslicki, David
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Shahrivar, Damon
    Walker, Alan W.
    Francis, Suzanna C.
    Fraser, Louise J.
    Vehkaperae, Mikko
    Lan, Yueheng
    Corander, Jukka
    ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 10, article id e0140644Article in journal (Refereed)
    Abstract [en]

    Motivation Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging. Results There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity. Availability An open source, platform-independent implementation of the method in the Julia programming language is freely available at https://github.com/dkoslicki/ARK. A Matlab implementation is available at http://www.ee.kth.se/ctsoftware.

  • 48.
    Kundu, Achintya
    et al.
    Indian Institute of Science.
    Chatterjee, Saikat
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    GMM based Bayesian approach to speech enhancement in signal/transform domain2008In: 2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, New York: IEEE , 2008, p. 4893-4896Conference paper (Refereed)
    Abstract [en]

    Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

  • 49.
    Kundu, Achintya
    et al.
    Indian Institute of Science.
    Chatterjee, Saikat
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Speech enhancement using intra- frame dependency in DCT domain2008Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a new speech enhancement approach, that is based on exploiting the intra-frame dependency of discrete cosine transform (DCT) domain coefficients. It can be noted that the existing enhancement techniques treat the transformdomain coefficients independently. Instead of this traditional approach of independently processing the scalars, we split the DCT domain noisy speech vector into sub-vectors and each sub-vector is enhanced independently. Through this sub-vector based approach, the higher dimensional enhancement advantage, viz. non-linear dependency, is exploited. In the developed method, each clean speech sub-vector is modeled using a Gaussian mixture (GM) density. We show that the proposed Gaussian mixture model (GMM) based DCT domain method, using sub-vector processing approach, provides better performance than the conventional approach of enhancing the transform domain scalar components independently. Performance improvement over the recently proposed GMM based time domain approach is also shown.

  • 50.
    Kundu, Achintya
    et al.
    Indian Institute of Science.
    Chatterjee, Saikat
    Indian Institute of Science.
    Sreenivas, T.V.
    Indian Institute of Science.
    Subspace based speech enhancement using Gaussian mixture model2008In: INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5, 2008, p. 395-398Conference paper (Refereed)
    Abstract [en]

    Traditional subspace based speech enhancement (SSE) methods use linear minimum mean square error (LMMSE) estimation that is optimal if the Karhunen Loeve transform (KLT) coefficients of speech and noise are Gaussian distributed. In this paper, we investigate the use of Gaussian mixture (GM) density for modeling the non-Gaussian statistics of the clean speech KLT coefficients. Using Gaussian mixture model (GMM), the optimum minimum mean square error (MMSE) estimator is found to be nonlinear and the traditional LMMSE estimator is shown to be a special case. Experimental results show that the proposed method provides better enhancement performance than the traditional subspace based methods.

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