IEEE Signal Processing Magazine (IEEE SIGNAL PROC MAG)

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

Journal description

The practical applications aspects of acoustics, speech, and signal processing.

Current impact factor: 5.85

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 5.852
2013 Impact Factor 4.481
2012 Impact Factor 3.368
2011 Impact Factor 4.066
2010 Impact Factor 5.86
2009 Impact Factor 4.914
2008 Impact Factor 3.758
2007 Impact Factor 2.907
2006 Impact Factor 2.655
2005 Impact Factor 2.714
2004 Impact Factor 3.707
2003 Impact Factor 4.241
2002 Impact Factor 3.298
2001 Impact Factor 1.981
2000 Impact Factor 1.185
1999 Impact Factor 2.256
1998 Impact Factor 1.879
1997 Impact Factor 0.943

Impact factor over time

Impact factor
Year

Additional details

5-year impact 5.88
Cited half-life 7.00
Immediacy index 1.33
Eigenfactor 0.01
Article influence 2.89
Website IEEE Signal Processing Magazine website
Other titles IEEE signal processing magazine, Institute of Electrical and Electronics Engineers signal processing magazine, Signal processing magazine, I.E.E.E. signal processing magazine, IEEE SP magazine
ISSN 1053-5888
OCLC 22582650
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Institute of Electrical and Electronics Engineers

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on Author's personal website, employers website or publicly accessible server
    • Author's post-print on Author's server or Institutional server
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    • Author's pre-print must be accompanied with set-phrase, when accepted by IEEE for publication ("(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")
    • IEEE must be informed as to the electronic address of the pre-print
    • If funding rules apply authors may post Author's post-print version in funder's designated repository
    • Author's Post-print - Publisher copyright and source must be acknowledged with citation (see above set statement)
    • Author's Post-print - Must link to publisher version with DOI
    • Publisher's version/PDF cannot be used
    • Publisher copyright and source must be acknowledged
  • Classification
    green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to more recent sensor network applications in surveillance, target localization, and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], [5].
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: Hearing enhancement is an area where signal processing technology makes a direct and positive impact on human lives. By allowing people with hearing loss to perceive voices, music, and other sounds more clearly and naturally, signal processing improves the quality of life for countless millions of people worldwide.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: Recounts the career and contributions of James L. Flanagan.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: Compressed sensing deals with the reconstruction of signals from sub-Nyquist samples by exploiting the sparsity of their projections onto known subspaces. In contrast, this article is concerned with the reconstruction of second-order statistics, such as covariance and power spectrum, even in the absence of sparsity priors. The framework described here leverages the statistical structure of random processes to enable signal compression and offers an alternative perspective at sparsity-agnostic inference. Capitalizing on parsimonious representations, we illustrate how compression and reconstruction tasks can be addressed in popular applications such as power-spectrum estimation, incoherent imaging, direction-of-arrival estimation, frequency estimation, and wideband spectrum sensing.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: The landscape of signal processing books is quite populated, and it is a tall order to produce a new book that can constructively add to this landscape. This book not only accomplishes this but also stands out for its distinct, consistent, and strong personality. The book???s identity starts from a presentation that is faithful to its title since the topics covered in this book are thorough in explaining the foundations of signal processing. However, foundations can be built in many ways and with different materials. The distinct element in this book is that these foundations are built based on the use of Hilbert space geometry, which allows extending Euclidean geometric insights to signals. As such, the geometry of Hilbert spaces forms the common thread across the multiple topics explained in the book. This allows for a presentation where the topics, such as Fourier representations, sampling, interpolation, approximation, compression, and filter design, can be seamlessly unified across finite dimensions, discrete time, and continuous time.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: ??????Since the late 1970s, numerous instances of design and applications of channelizers or channelization receivers have been widely reported. With the advent of high-speed field programmable gate arrays, new-generation architectures for implementation with reduced hardware have emerged in the past decade. However, these discrete processing techniques have evolved independently of the classical analog techniques, which were used until a generation earlier. These digital processing methods are not the digitized forms of their analog counterparts and are far superior in performance and flexibility in comparison to the legacy systems. Most of the reported literature in this area requires a level of analytical comprehension that may be too demanding for a student at an entry level in the subject. Keeping this in mind, we are, in this lecture note, presenting an alternative graphical analysis of a polyphase channelizer with a four-channel case study. This example can be easily generalized for an M-channels receiver case.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: This article presents a powerful algorithmic framework for big data optimization, called the block successive upper-bound minimization (BSUM). The BSUM includes as special cases many well-known methods for analyzing massive data sets, such as the block coordinate descent (BCD) method, the convex-concave procedure (CCCP) method, the block coordinate proximal gradient (BCPG) method, the nonnegative matrix factorization (NMF) method, the expectation maximization (EM) method, etc. In this article, various features and properties of the BSUM are discussed from the viewpoint of design flexibility, computational efficiency, parallel/distributed implementation, and the required communication overhead. Illustrative examples from networking, signal processing, and machine learning are presented to demonstrate the practical performance of the BSUM framework.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: This article presents novel tricks regarding cascading two digital filters to produce composite filters with very sharp transition bands for high-performance applications. The key point of the proposed tricks is to shape the magnitude frequency response of a prototype infinite impulse response (IIR) filter by a two-tap finite impulse response (FIR) filter using its nulls. In particular, we choose either a comb filter or a complementary comb filter of coefficients +1/-1, also called a shaping filter, to sharpen the transition bands of a prototype filter. The magnitude frequency response of the shaping filter compensates the Gibbs phenomenon commonly appearing in the passband edge and produces sharp transition bands for the cascaded filter. As compared to an equivalent IIR filter, the price paid is an additional comb/complementary comb filter of low complexity.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: Presents the President's message for this issue of the publication.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: The recovery and analysis of digital information has become a major component of many criminal investigations today. Given the ever-increasing number of personal digital devices, such as notebooks, tablets, and smartphones, as well as the development of communication infrastructures, we all gather, store, and generate huge amounts of data. Some of this information may be precious evidence for investigation and may be used in courts. During the last several decades, increasing research efforts have therefore been dedicated toward defining tools and protocols for the analysis of evidence coming from digital sources. This book attempts to link research in these two communities by providing a wide-ranging and up-to-date reference for both researchers and practitioners. The digital forensics ecosystem is surveyed with the necessary breadth in the first half of the book, by exploring all phases of the forensics workflow and detailing several tools of interest. Gaining insight into these aspects is of paramount importance for practitioners, but also for academic researchers who are often not aware of the standard practices and processes required to preserve digital evidence, e.g., for legal purposes. Similarly, practitioners have the opportunity to discover the state of the art in forensics research in the second half of the book, which is written from a signal processing perspective. This balanced mix is a major asset of this book, making it suitable for readers of diverse background.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: With the increasing ubiquity and power of mobile devices as well as the prevalence of social systems, more activities in our daily life are being recorded, tracked, and shared, creating the notion of social media. Such abundant and still growing real-life data, known as big data, provide a tremendous research opportunity in many fields. To analyze, learn, and understand such user-generated data, machine learning has been an important tool, and various machine-learning algorithms have been developed. However, since the user-generated data are the outcome of users? decisions, actions, and socioeconomic interactions, which are highly dynamic, without considering users? local behaviors and interests, existing learning approaches tend to focus on optimizing a global objective function at the macroeconomic level, while totally ignoring users? local interactions at the microeconomic level. As such, there is a growing need to combine learning with strategic decision making, which are two traditionally distinct research disciplines, to be able to jointly consider both global phenomena and local effects to better understand, model, and analyze the newly arising issues in the emerging social media with user-generated data. In this article, we present an overview of the emerging notion of decision learning, i.e., learning with strategic decision making, which involves users? behaviors and interactions by combining learning with strategic decision making. We will discuss some examples from social media with real data to show how decision learning can be used to better analyze users? optimal decision from a user?s perspective, as well as design a mechanism from the system designer?s perspective to achieve a desirable outcome.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: Presents the introductory editorial for this issue of the publication.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: Although the current digital terrestrial broadcast system (now referred to as ATSC 1.0) is widespread and quite successful, the basic component technologies have been in use for 20 years. Technology has evolved and viewer expectations have changed. Television broadcasters are under increasing pressure, due to regulatory and spectrum issues, as well as increasing competition for the viewer?s attention. For these reasons, the Advanced Television Committee (ATSC) has been working on the next-generation broadcast television system, known as ATSC 3.0.
    No preview · Article · Jan 2016 · IEEE Signal Processing Magazine
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    ABSTRACT: For decades, signal processing has played a key role in the development of sophisticated consumer products. From personal audio and video systems to cameras to smartphones to satellite navigation systems and beyond, signal processing has helped manufacturers worldwide develop a wide range of innovative and affordable consumer devices.
    No preview · Article · Nov 2015 · IEEE Signal Processing Magazine
  • [Show abstract] [Hide abstract]
    ABSTRACT: Presents the President???s Message for this issue of the publication.
    No preview · Article · Nov 2015 · IEEE Signal Processing Magazine