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

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: 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: 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: 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: 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: 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: 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
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    ABSTRACT: In this paper, we study the time-of-arrival (TOA) based self-calibration problem of dual-microphone array for known and unknown rack distance, and also for different combinations of dimension for the affine spaces spanned by the receivers and by the senders. Particularly, we analyze the minimum cases and present minimum solvers for the case of microphones and speakers in 3-D/3-D, in 2-D/3-D, and in 3-D/2-D, with given or unknown rack length. We identify for each of these minimal problems the number of solutions in general and develop efficient and numerically stable, non-iterative solvers. Solving these problems are of both theoretical and practical interest. This includes understanding what the minimal problems are and how and when they can be solved. The solvers can be used to initialize local optimization algorithms for finding the maximum likelihood estimate of the parameters. The solvers can also be used for robust estimation of the parameters in the presence of outliers, using, e.g., RANSAC algorithms. We demonstrate that the proposed solvers are numerically stable in synthetic experiments. We also demonstrate how the solvers can be used with the RANSAC paradigm. We also apply our method for several real data experiments, using ultra-wide-band measurements and using acoustic data.
    IEEE Journal of Selected Topics in Signal Processing 08/2015; 9(5):1-1. DOI:10.1109/JSTSP.2015.2417117
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    ABSTRACT: This paper presents a design method for microphone arrays with arbitrary geometries. Based on a theoretical analysis and on the magic points method, it allows for the interpolation of a sound field in a generic convex domain with a limited number of microphones on a given frequency band. It is shown that only a few microphones are needed in the interior of the considered domain to ensure a low interpolation error in the frequency band of interest, and that most of the microphones have to be located on the boundary of the domain, with a non-uniform density depending on the shape of the domain. Practical design constraints can be included in the optimization process. Comparisons for some particular array geometries with design methods known from the literature are given, showing that the proposed approach results in lower errors.
    IEEE Journal of Selected Topics in Signal Processing 08/2015; 9(5):1-1. DOI:10.1109/JSTSP.2015.2412097
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    ABSTRACT: This paper addresses the problem of rumor source detection with multiple observations, from a statistical point of view of a spreading over a network, based on the susceptible-infectious model. For tree networks, multiple independent observations can dramatically improve the detection probability. For the case of a single rumor source, we propose a unified inference framework based on the joint rumor centrality, and provide explicit detection performance for degree-regular tree networks. Surprisingly, even with merely two observations, the detection probability at least doubles that of a single observation, and further approaches one, i.e., reliable detection, with increasing degree. This indicates that a richer diversity enhances detectability. Furthermore, we consider the case of multiple connected sources and investigate the effect of diversity. For general graphs, a detection algorithm using a breadth-first search strategy is also proposed and evaluated. Besides rumor source detection, our results can be used in network forensics to combat recurring epidemic-like information spreading such as online anomaly and fraudulent email spams.
    IEEE Journal of Selected Topics in Signal Processing 06/2015; 9(4):1-1. DOI:10.1109/JSTSP.2015.2389191
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    ABSTRACT: With the vast availability of traffic sensors from which traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning, etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations, which may differ from that of the historical data and change over time. In this paper, we propose a novel online framework that could learn from the current traffic situation (or context) in real-time and predict the future traffic by matching the current situation to the most effective prediction model trained using historical data. As real-time traffic arrives, the traffic context space is adaptively partitioned in order to efficiently estimate the effectiveness of each base predictor in different situations. We obtain and prove both short-term and long-term performance guarantees (bounds) for our online algorithm. The proposed algorithm also works effectively in scenarios where the true labels (i.e., realized traffic) are missing or become available with delay. Using the proposed framework, the context dimension that is the most relevant to traffic prediction can also be revealed, which can further reduce the implementation complexity as well as inform traffic policy making. Our experiments with real-world data in real-life conditions show that the proposed approach significantly outperforms existing solutions.
    IEEE Journal of Selected Topics in Signal Processing 06/2015; 9(4):1-1. DOI:10.1109/JSTSP.2015.2389196
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    ABSTRACT: We present structured data fusion (SDF) as a framework for the rapid prototyping of knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set—stored as a dense, sparse, or incomplete tensor—is factorized with a matrix or tensor decomposition. Factorizations can be coupled, or fused, with each other by indicating which factors should be shared between data sets. At the same time, factors may be imposed to have any type of structure that can be constructed as an explicit function of some underlying variables. With the right choice of decomposition type and factor structure, even well-known matrix factorizations such as the eigenvalue decomposition, singular value decomposition and QR factorization can be computed with SDF. A domain specific language (DSL) for SDF is implemented as part of the software package Tensorlab, with which we offer a library of tensor decompositions and factor structures to choose from. The versatility of the SDF framework is demonstrated by means of four diverse applications, which are all solved entirely within Tensorlab’s DSL.
    IEEE Journal of Selected Topics in Signal Processing 06/2015; 9(4):586-600. DOI:10.1109/JSTSP.2015.2400415
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    ABSTRACT: Sparsity-based techniques have been widely popular in signal processing applications such as compression, denoising, and compressed sensing. Recently, the learning of sparsifying transforms for data has received interest. The advantage of the transform model is that it enables cheap and exact computations. In Part I of this work, efficient methods for online learning of square sparsifying transforms were introduced and investigated (by numerical experiments). The online schemes process signals sequentially, and can be especially useful when dealing with big data, and for real-time, or limited latency signal processing applications. In this paper, we prove that although the associated optimization problems are non-convex, the online transform learning algorithms are guaranteed to converge to the set of stationary points of the learning problem. The guarantee relies on a few simple assumptions. In practice, the algorithms work well, as demonstrated by examples of applications to representing and denoising signals.
    IEEE Journal of Selected Topics in Signal Processing 06/2015; 9(4):637-646. DOI:10.1109/JSTSP.2015.2407860
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    ABSTRACT: We propose three novel algorithms for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. Specifically, we describe methods that learn the projection of data and find the sparse and/or low-rank coefficients in the low-dimensional latent space. Cluster labels are then assigned by applying spectral clustering to a similarity matrix built from these representations. Efficient optimization methods are proposed and their non-linear extensions based on kernel methods are presented. Various experiments show that the proposed methods perform better than many competitive subspace clustering methods.
    IEEE Journal of Selected Topics in Signal Processing 06/2015; 9(4):691-701. DOI:10.1109/JSTSP.2015.2402643
  • [Show abstract] [Hide abstract]
    ABSTRACT: With the Internet, social media, wireless mobile devices, and pervasive sensors continuously collecting massive amounts of data, we undoubtedly live in an era of ---data deluge.--- Learning from such huge volumes of data however, promises ground-breaking advances in science and engineering along with consequent improvements in quality of life. Indeed, mining information from big data could limit the spread of epidemics and diseases, identify trends in financial and e-markets, unveil topologies and dynamics of emergent social-computational systems, accelerate brain imaging, neuroscience and systems biology models, and also protect critical infrastructure including the power grid and the Internet's backbone network. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. Given these challenges, ample signal processing opportunities arise. The articles in this special section explore novel modeling approaches, algorithmic advances along with their performance analysis, as well as representative applications of Big Data analytics to address practical challenges, while revealing fundamental limits and insights on the analytical trade-offs involved.
    IEEE Journal of Selected Topics in Signal Processing 06/2015; 9(4):583-585. DOI:10.1109/JSTSP.2015.2418393