IEEE Journal of Selected Topics in Signal Processing Impact Factor & Information

Publisher: Institute of Electrical and Electronics Engineers; IEEE Signal Processing Society, Institute of Electrical and Electronics Engineers

Journal description

Current impact factor: 3.63

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 3.629
2012 Impact Factor 3.297
2011 Impact Factor 2.88
2010 Impact Factor 2.571
2009 Impact Factor 1.2

Impact factor over time

Impact factor
Year

Additional details

5-year impact 3.82
Cited half-life 3.50
Immediacy index 0.32
Eigenfactor 0.02
Article influence 1.99
Other titles IEEE journal of selected topics in signal processing, Selected topics in signal processing
ISSN 1932-4553
OCLC 158906070
Material type Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Institute of Electrical and Electronics Engineers

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  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper introduces a new unsupervised method for dimensionality reduction via regression (DRR). The algorithm belongs to the family of invertible transforms that generalize principal component analysis (PCA) by using curvilinear instead of linear features. DRR identifies the nonlinear features through multivariate regression to ensure the reduction in redundancy between the PCA coefficients, the reduction of the variance of the scores, and the reduction in the reconstruction error. More importantly, unlike other nonlinear dimensionality reduction methods, the invertibility, volume-preservation, and straightforward out-of-sample extension, makes DRR interpretable and easy to apply. The properties of DRR enable learning a more broader class of data manifolds than the recently proposed non-linear principal components analysis (NLPCA) and principal polynomial analysis (PPA). We illustrate the performance of the representation in reducing the dimensionality of remote sensing data. In particular, we tackle two common problems: processing very high dimensional spectral information such as in hyperspectral image sounding data, and dealing with spatial-spectral image patches of multispectral images. Both settings pose collinearity and ill-determination problems. Evaluation of the expressive power of the features is assessed in terms of truncation error, estimating atmospheric variables, and surface land cover classification error. Results show that DRR outperforms linear PCA and recently proposed invertible extensions based on neural networks (NLPCA) and univariate regressions (PPA).
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2417833
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    ABSTRACT: Hyperspectral image classification has attracted extensive research efforts in the recent decades. The main difficulty lies in the few labeled samples versus high dimensional features. The spectral-spatial classification method using Markov random field (MRF) has been shown to perform well in improving the classification performance. Moreover, active learning (AL), which iteratively selects the most informative unlabeled samples and enlarges the training set, has been widely studied and proven useful in remotely sensed data. In this paper, we focus on the combination of MRF and AL in the classification of hyperspectral images, and a new MRF model-based AL (MRF-AL) framework is proposed. In the proposed framework, the unlabeled samples whose predicted results vary before and after the MRF processing step is considered as uncertain. In this way, subset is firstly extracted from the entire unlabeled set, and AL process is then performed on the samples in the subset. Moreover, hybrid AL methods which combine the MRF-AL framework with either the passive random selection method or the existing AL methods are investigated. To evaluate and compare the proposed AL approaches with other state-of-the-art techniques, experiments were conducted on two hyperspectral data sets. Results demonstrated the effectiveness of the hybrid AL methods, as well as the advantage of the proposed MRF-AL framework.
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2414401
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    ABSTRACT: We address the problem of unsupervised clustering of multidimensional data when the number of clusters is not known a priori. The proposed iterative approach is a stochastic extension of the density-based clustering (knnclust) method which randomly assigns objects to clusters by sampling a posterior class label distribution. In our approach, contextual class-conditional distributions are estimated based on a nearest neighbors graph, and are iteratively modified to account for current cluster labeling. Posterior probabilities are also slightly reinforced to accelerate convergence to a stationary labeling. A stopping criterion based on the measure of clustering entropy is defined thanks to the Kozachenko-Leonenko differential entropy estimator, computed from current class-conditional entropies. One major advantage of our approach relies in its ability to provide an estimate of the number of clusters present in the data set. The application of our approach to the clustering of real hyperspectral image data is considered. Our algorithm is compared with other unsupervised clustering approaches, namely affinity propagation (ap), knnclust and Non Parametric Stochastic Expectation Maximization (npsem), and is shown to improve the correct classification rate in most experiments.
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2413371
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    ABSTRACT: The main goal of remote sensing image classification is to associate land cover classes to each pixel in the monitored area. In this sense, hyperspectral images play a key role by providing detailed spectral information per pixel. On the other hand, although the huge amount of spectral bands enables the creation of more accurate thematic maps, they can compromise the quality of results due to data redundancy, high-dimensionality problems and noisy bands. Many dimensionality reduction techniques have been proposed in order to better use the available information. An effective strategy is to perform a band selection, which aims at selecting the best bands for classification. This process decreases the dimensionality without degrading information, i.e., it keeps the physical properties acquired by the sensors. As a drawback, the dimensionality reduction process can take a lot of time to be performed. In this paper, we propose a new unsupervised band selection method based on the dissimilarity among neighboring bands by exploiting an intermediary representation called spectral rhythm. Our approach can take advantage of a pixel sampling strategy to improve its efficiency without significant reduction on the quality of selected bands. Experimental results reveal that our method can efficiently select suitable bands to represent the whole data by producing accuracy results as good as the baselines in the classification problem.
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2405902
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    ABSTRACT: This paper presents strategies for spectral de- noising of hyperspectral images and 3-D data cube reconstruction from a limited number of tomographic measurements, arising in single snapshot imaging systems. For de-noising the main idea is to exploit the incoherency between the algebraic complexity measure, namely the low rank of the noise-free hyperspectral data cube, and the sparsity structure of the spectral noise. In particular, the non-noisy spectral data, when stacked across the spectral dimension, exhibits low-rank due to a small number of species. On the other hand, under the same representation, the spectral noise exhibits a banded structure. Motivated by this we show that the de-noised spectral data and the unknown spectral noise and the respective bands can be simultaneously estimated through the use of a low-rank and simultaneous sparse minimization operation without prior knowledge of the noisy bands. This result is novel for hyperspectral imaging applications and we compare our results with several existing methods for noisy band recovery. For recovery under limited tomographic projections we exploit both the low algebraic and structural complexity of the data cube via joint rank penalization plus Total Variation/wavelet domain sparsity, which is novel for single snapshot hyperspectral imaging systems. We combine these two approaches for simultaneous spectral de-noising and data cube recovery under limited measurements. We perform extensive simulations and our main result indicates that exploiting both low algebraic and structural complexity has a superior performance compared to exploiting only the structural complexity. To address the computational challenges associated with the resulting optimization problem we adapt several recent developments in the area of convex optimization, specifically employing splitting and proximal point based methods.
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2428678
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    ABSTRACT: Sub-pixel mapping is a useful technique for providing land-cover information at the sub-pixel scale by the use of the input fraction image at a coarse resolution. Some sub-pixel mapping algorithms with strict consideration of the abundance constraint have difficulty in obtaining a satisfactory performance in sub-pixel mapping since the fraction image obtained by spectral unmixing always contains errors. In this paper, in order to make full use of the input fraction image and alleviate the effect of fraction errors, a sub-pixel mapping algorithm based on conditional random fields (CRFSM) is proposed for hyperspectral remote sensing imagery. The CRFSM algorithm fuses the local spatial prior at the fine scale and the downscaled coarse fraction at the coarse scale by potential functions to obtain more detailed land-cover distribution information. The local spatial prior models the local spatial structure to obtain the local land-cover features at the fine scale. The downscaled coarse fraction considers the fraction values to maintain the holistic land-cover features at the coarse scale. In addition, the abundance constraint is considered as a soft constraint by the probability class determination strategy in the CRFSM algorithm, to help with the class label determination of sub-pixels and alleviate the effect of the fraction errors and noise. The experimental results with two synthetic hyperspectral images and a real Nuance hyperspectral image show that the proposed sub-pixel mapping algorithm has a competitive performance in both the quantitative and qualitative evaluations, compared with the other state-of-the-art sub-pixel mapping algorithms.
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2416683
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    ABSTRACT: In sparse representation (SR) driven hyperspectral image classification, signal-to-reconstruction rule-based classification may lack generalization performance. In order to overcome this limitation, we presents a new method for discriminative sparse representation of hyperspectral data by learning a reconstructive dictionary and a discriminative classifier in a SR model regularized with total variation (TV). The proposed method features the following components. First, we adopt a spectral unmixing by variable splitting augmented Lagrangian and TV method to guarantee the spatial homogeneity of sparse representations. Second, we embed dictionary learning in the method to enhance the representative power of sparse representations via gradient descent in a class-wise manner. Finally, we adopt a sparse multinomial logistic regression (SMLR) model and design a class-oriented optimization strategy to obtain a powerful classifier, which improves the performance of the learnt model for specific classes. The first two components are beneficial to produce discriminative sparse representations. Whereas, adopting SMLR allows for effectively modeling the discriminative information. Experimental results with both simulated and real hyperspectral data sets in a number of experimental comparisons with other related approaches demonstrate the superiority of the proposed method.
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2423260
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    ABSTRACT: The separability assumption turns the nonnegative matrix factorization (NMF) problem tractable, which coincides with the pure pixel assumption and provides new insights for the hyperspectral unmixing problem. Based on this assumption, and starting from the data self-expressiveness perspective, we formulate the unmixing problem as a joint sparse recovery problem by using the data itself as a dictionary. Moreover, we present a quasi-greedy algorithm for this problem by employing a back-tracking strategy. In comparison with the previous greedy methods, the proposed method can refresh the candidate pixels by solving a small fixed-scale convex sub-problem in every iteration. Therefore, our method has two important characteristics: (i) enhanced robustness against noise; (ii) moderate computational complexity and scalability to large dataset. Finally, computer simulations on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.
    IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):1-1. DOI:10.1109/JSTSP.2015.2419184
  • IEEE Journal of Selected Topics in Signal Processing 09/2015; 9(6):961-963. DOI:10.1109/JSTSP.2015.2457631
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    ABSTRACT: A novel localization approach is proposed in order to find the position of an individual source using recordings of a single microphone in a reverberant enclosure. The multipath propagation is modeled by multiple virtual microphones as images of the actual single microphone and a multipath distance matrix is constructed whose components consist of the squared distances between the pairs of microphones (real or virtual) or the squared distances between the microphones and the source. The distances between the actual and virtual microphones are computed from the geometry of the enclosure. The microphone-source distances correspond to the support of the early reflections in the room impulse response associated with the source signal acquisition. The low-rank property of the Euclidean distance matrix is exploited to identify this correspondence. Source localization is achieved through optimizing the location of the source matching those measurements. The recording time of the microphone and generation of the source signal is asynchronous and estimated via the proposed procedure. Furthermore, a theoretically optimal joint localization and synchronization algorithm is derived by formulating the source localization as minimization of a quartic cost function. It is shown that the global minimum of the proposed cost function can be efficiently computed by converting it to a generalized trust region sub-problem. Numerical simulations on synthetic data and real data recordings obtained by practical tests show the effectiveness of the proposed approach.
    IEEE Journal of Selected Topics in Signal Processing 08/2015; 9(5):1-1. DOI:10.1109/JSTSP.2015.2422677
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    ABSTRACT: One big challenge to the robust direction of arrival (DOA) estimation for the speech source is the environmental noise. In practical conditions, the noise can be undirected or emitted from a pointed source. In order to improve the reliability of DOA estimation in various adverse noisy conditions, we propose a novel DOA estimation method in this paper, and what lies in the core in the method is the “Weighted Bispectrum Spatial Correlation Matrix (WBSCM).” The bispectrum is a kind of higher order statistics (HOS) of a signal, and the WBSCM reflects the spatial correlation of the bispectrum phase differences (BPD) between different microphones. As the HOS of the Gaussian signal is theoretically zero, by formulating in the bispectrum domain, the proposed method has an inherent advantage against the Gaussian noise. Moreover, the BPD, which is embedded in the WBSCM, contains the redundant information related to the DOA of the speech source. This redundancy helps to improve the robustness in non-Gaussian noise conditions, especially for the directional interference scenarios. In addition, the WBSCM enables bispectrum weighting to select the speech units in the bispectrum, in order to highlight the effect of these units in the DOA estimation. Similar to the signal-to-noise estimation, a decision-directed method is proposed to compute the bispectrum weights. Finally, a new DOA estimator is proposed, which is based on the eigenvalue analysis of the WBSCM. We conduct experiments under various kinds of noisy environments, and the experimental results demonstrate the effectiveness of proposed method.
    IEEE Journal of Selected Topics in Signal Processing 08/2015; 9(5):1-1. DOI:10.1109/JSTSP.2015.2416686
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    ABSTRACT: Several methods have recently been proposed for modeling spatially continuous head-related transfer functions (HRTFs) using techniques based on finite-order spherical harmonic expansion. These techniques inherently impart some amount of spatial smoothing to the measured HRTFs. However, the effect this spatial smoothing has on the localization accuracy has not been analyzed. Consequently, the relationship between the order of a spherical harmonic representation for HRTFs and the maximum localization ability that can be achieved with that representation remains unknown. The present study investigates the effect that spatial smoothing has on virtual sound source localization by systematically reducing the order of a spherical-harmonic-based HRTF representation. Results of virtual localization tests indicate that accurate localization performance is retained with spherical harmonic representations as low as fourth-order, and several important physical HRTF cues are shown to be present even in a first-order representation. These results suggest that listeners do not rely on the fine details in an HRTF's spatial structure and imply that some of the theoretically-derived bounds for HRTF sampling may be exceeding perceptual requirements.
    IEEE Journal of Selected Topics in Signal Processing 08/2015; 9(5):1-1. DOI:10.1109/JSTSP.2015.2421876
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    ABSTRACT: To provide immersive 3D multimedia service, MPEG has launched MPEG-H, ISO/IEC 23008, “High Efficiency Coding and Media Delivery in Heterogeneous Environments.” As part of the audio, MPEG-H 3D Audio has been standardized based on a multichannel loudspeaker configuration (e.g., 22.2). Binaural rendering is a key application of 3D audio; however, previous studies focus on binaural rendering with low complexity such as IIR filter design for HRTF or pre-/post-processing to solve in-head localization or front-back confusion. In this paper, a new binaural rendering algorithm is proposed to support the large number of input channel signals and provide high-quality in terms of timbre, parts of this algorithm were adopted into the MPEG-H 3D Audio. The proposed algorithm truncates binaural room impulse response at mixing time, the transition point from the early-reflections to the late reverberation part. Each part is processed independently by variable order filtering in frequency domain (VOFF) and parametric late reverberation filtering (PLF), respectively. Further, a QMF domain tapped delay line (QTDL) is proposed to reduce complexity in the high-frequency band, based on human auditory perception and codec characteristics. In the proposed algorithm, a scalability scheme is adopted to cover a wide range of applications by adjusting the threshold of mixing time. Experimental results show that the proposed algorithm is able to provide the audio quality of a binaural rendered signal using full-length binaural room impulse responses. A scalability test also shows that the proposed scalability scheme smoothly compromises between audio quality and computational complexity.
    IEEE Journal of Selected Topics in Signal Processing 08/2015; 9(5):1-1. DOI:10.1109/JSTSP.2015.2425799
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    ABSTRACT: Virtual auditory displays are systems that use signal processing techniques to manipulate the apparent spatial locations of sounds when they are presented to listeners over headphones. When the virtual audio display is limited to the presentation of stationary sounds at a finite number of source locations, it is possible to produce virtual sounds that are essentially indistinguishable from sounds presented by real loudspeakers in the free field. However, when the display is required to reproduce sound sources at arbitrary locations and respond in real-time to the head motions of the listener, it becomes much more difficult to maintain localization performance that is equivalent to the free field. The purpose of this paper is to present the results of a study that used a virtual synthesis technique to produce head-tracked virtual sounds that were comparable in terms of localization performance with real sound sources. The technique made use of an in-situ measurement and reproduction technique that made it possible to switch between the head-related transfer function measurement and the psychoacoustic validation without removing the headset from the listener. The results demonstrate the feasibility of using head-tracked virtual auditory displays to generate both short and long virtual sounds with localization performance comparable to what can be achieved in the free field.
    IEEE Journal of Selected Topics in Signal Processing 08/2015; 9(5):1-1. DOI:10.1109/JSTSP.2015.2421874
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    ABSTRACT: The science and art of Spatial Audio is concerned with the capture, production, transmission, and reproduction of an immersive sound experience. Recently, a new generation of spatial audio technology has been introduced that employs elevated and lowered loudspeakers and thus surpasses previous ‘surround sound’ technology without such speakers in terms of listener immersion and potential for spatial realism. In this context, the ISO/MPEG standardization group has started the MPEG-H 3D Audio development effort to facilitate high-quality bitrate-efficient production, transmission and reproduction of such immersive audio material. The underlying format is designed to provide universal means for carriage of channel-based, object-based and Higher Order Ambisonics based input. High quality reproduction is provided for many output formats from 22.2 and beyond down to 5.1, stereo and binaural reproduction—independently of the original encoding format, thus overcoming the incompatibility between various 3D formats. This paper provides an overview of the MPEG-H 3D Audio project and technology and an assessment of the system capabilities and performance.
    IEEE Journal of Selected Topics in Signal Processing 08/2015; 9(5):1-1. DOI:10.1109/JSTSP.2015.2411578