IEEE Signal Processing Magazine Journal Impact Factor & Information

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

2015 Impact Factor Available summer 2016
2014 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

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, once submitted to IEEE for publication ("This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible")
    • 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
    ‚Äč green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: EEG and MEG are the most common noninvasive brain imaging techniques for monitoring the electrical brain activity and inferring the brain function. The central goal of EEG/MEG analysis is to extract informative brain spatio-temporal-spectral patterns or to infer functional connectivity between different brain areas, which are directly useful for neuroscience or clinical investigations. Due to its potentially complex nature (such as nonstationarity, high-dimensionality, subject variability, low signal-to-noise ratio), EEG/MEG signal processing poses some great challenges for researchers. These challenges can be addressed in a principled manner via Bayesian machine learning (BML). BML is an emerging field that integrates Bayesian statistics, variational methods, and machine learning techniques to solve various problems from regression, prediction, outlier detection, feature extraction and classification. BML has recently gained increasing attention and widespread successes in signal processing and big data analytics, such as in source reconstruction, compressed sensing, and information fusion. To review recent advances and to foster new research ideas, we provide a tutorial on several important emerging BML research topics in EEG/MEG signal processing and present representative examples in EEG/MEG applications.
    IEEE Signal Processing Magazine 12/2015;
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    ABSTRACT: Biometric recognition is an integral component of modern identity management and access control systems. Due to the strong and permanent link between individuals and their biometric traits, exposure of enrolled users? biometric information to adversaries can seriously compromise biometric system security and user privacy. Numerous techniques have been proposed for biometric template protection over the last 20 years. While these techniques are theoretically sound, they seldom guarantee the desired noninvertibility, revocability, and nonlinkability properties without significantly degrading the recognition performance. The objective of this work is to analyze the factors contributing to this performance divide and highlight promising research directions to bridge this gap. The design of invariant biometric representations remains a fundamental problem, despite recent attempts to address this issue through feature adaptation schemes. The difficulty in estimating the statistical distribution of biometric features not only hinders the development of better template protection algorithms but also diminishes the ability to quantify the noninvertibility and nonlinkability of existing algorithms. Finally, achieving nonlinkability without the use of external secrets (e.g., passwords) continues to be a challenging proposition. Further research on the above issues is required to cross the chasm between theory and practice in biometric ?template protection.
    IEEE Signal Processing Magazine 09/2015; 32(5):88-100. DOI:10.1109/MSP.2015.2427849
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    ABSTRACT: Recent years have seen an exponential growth in the use of various biometric technologies for trusted automatic recognition of humans. With the rapid adaptation of biometric systems, there is a growing concern that biometric technologies may compromise the privacy and anonymity of individuals. Unlike credit cards and passwords, which can be revoked and reissued when compromised, biometrics are permanently associated with a user and cannot be replaced. To prevent the theft of biometric patterns, it is desirable to modify them through revocable and noninvertible transformations to produce cancelable biometric templates. In this article, we provide an overview of various cancelable biometric schemes for biometric template protection. We discuss the merits and drawbacks of available cancelable biometric systems and identify promising avenues of research in this rapidly evolving field.
    IEEE Signal Processing Magazine 09/2015; 32(5):54-65. DOI:10.1109/MSP.2015.2434151
  • IEEE Signal Processing Magazine 09/2015; 32(5):109-111. DOI:10.1109/MSP.2015.2435816
  • IEEE Signal Processing Magazine 09/2015; 32(5):31-41. DOI:10.1109/MSP.2015.2426728
  • Min Wu
    IEEE Signal Processing Magazine 09/2015; 32(5):4-4. DOI:10.1109/MSP.2015.2449285
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    ABSTRACT: Biometrics refers to physiological (i.e., face, fingerprint, hand geometry, etc.) and behavioral (i.e., speech, signature, keystroke, etc.) traits of a human identity. As these traits are unique to individuals, biometrics can be used to identify users reliably in many authentication applications, such as access control and e-commerce. Most biometric authentication systems offer great convenience without requiring the users to possess or remember any secret credentials. For applications that demand greater security, biometrics can be used in complement with passwords and security tokens to offer a multifactor authentication.
    IEEE Signal Processing Magazine 09/2015; 32(5):77-87. DOI:10.1109/MSP.2015.2423693
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    ABSTRACT: Biometrics already form a significant component of current and emerging identification technologies. Biometrics systems aim to determine or verify the identity of an individual from their behavioral and/or biological characteristics. Despite significant progress, some biometric systems fail to meet the multitude of stringent security and robustness requirements to support their deployment in some practical scenarios. Among current concerns are vulnerabilities to spoofing?persons who masquerade as others to gain illegitimate accesses to protected data, services, or facilities. While the study of spoofing, or rather antispoofing, has attracted growing interest in recent years, the problem is far from being solved and will require far greater attention in the coming years. This tutorial article presents an introduction to spoofing and antispoofing research. It describes the vulnerabilities, presents an evaluation methodology for the assessment of spoofing and countermeasures, and outlines research priorities for the future.
    IEEE Signal Processing Magazine 09/2015; 32(5):20-30. DOI:10.1109/MSP.2015.2437652
  • [Show abstract] [Hide abstract]
    ABSTRACT: The importance of signal processing in imaging is growing rapidly as technologies continue to develop and mature and as various fields begin to recognize the value of innovative new imaging and image analysis systems. By enabling people to clearly observe and detect things that are not ordinarily visible or not readily apparent to the unaided eye, signal processing-driven imaging technologies are helping to save lives and property from hazards lurking both on the ground and below the earth?s surface.
    IEEE Signal Processing Magazine 09/2015; 32(5):8-18. DOI:10.1109/MSP.2015.2437291
  • [Show abstract] [Hide abstract]
    ABSTRACT: Biometric systems provide a valuable service in helping to identify individuals from their stored personal details. Unfortunately, with the rapidly increasing use of such systems [1], there is a growing concern about the possible misuse of that information. To counteract the threat, the European Union (EU) has introduced comprehensive legislation [2] that seeks to regulate data collection and help strengthen an individual?s right to privacy. This article looks at the implications of the legislation for biometric system deployment. After an initial consideration of current privacy concerns, the definition of ?personal data? and its protection is examined in legislative terms. Also covered are the issues surrounding the storage of biometric data, including its accuracy, its security, and justification for what is collected. Finally, the privacy issues are illustrated through three biometric use cases: border security, online bank access control, and customer profiling in stores.
    IEEE Signal Processing Magazine 09/2015; 32(5):101-108. DOI:10.1109/MSP.2015.2426682
  • [Show abstract] [Hide abstract]
    ABSTRACT: Systems employing biometric traits for people authentication and identification are witnessing growing popularity due to the unique and indissoluble link between any individual and his/her biometric characters. For this reason, biometric templates are increasingly used for border monitoring, access control, membership verification, and so on. When employed to replace passwords, biometrics have the added advantage that they do not need to be memorized and are relatively hard to steal. Nonetheless, unlike conventional security mechanisms such as passwords, biometric data are inherent parts of a person?s body and cannot be replaced if they are compromised. Even worse, compromised biometric data can be used to have access to sensitive information and to impersonate the victim for malicious purposes. For the same reason, biometric leakage in a given system can seriously jeopardize the security of other systems based on the same biometrics. A further problem associated with the use of biometric traits is that, due to their uniqueness, the privacy of their owner is put at risk. Geographical position, movements, habits, and even personal beliefs can be tracked by observing when and where the biometric traits of an individual are used to identify him/her.
    IEEE Signal Processing Magazine 09/2015; 32(5):66-76. DOI:10.1109/MSP.2015.2438131
  • [Show abstract] [Hide abstract]
    ABSTRACT: Presents the President's Message for this issue of the publication regarding the adoption of new peer-review models for research papers.
    IEEE Signal Processing Magazine 09/2015; 32(5):6-6. DOI:10.1109/MSP.2015.2449286
  • [Show abstract] [Hide abstract]
    ABSTRACT: The articles in this special section were conceived to champion recent developments in the rapidly evolving field of biometrics and also to encourage research in new signal processing solutions to security and privacy protection. Biometrics is the science of recognizing individuals based on their behavioral and biological characteristics such as face, fingerprints, iris, voice, gait, and signature. The past decade has witnessed a rapid increase in biometrics research in addition to the deployment of large-scale biometrics solutions in both civilian and law enforcement applications. Example applications that incorporate biometric recognition include: logical and physical access systems; surveillance operations to fight against fraud and organized crime; immigration control and border security systems; national identity programs; identity management systems; and the determination of friend or foe in military installations.
    IEEE Signal Processing Magazine 09/2015; 32(5):17-18. DOI:10.1109/MSP.2015.2443271
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    ABSTRACT: Presents an historical account of the Abbey Road recording studios in England where the Beatles recorded their music. Abbey Road Studios have been an iconic landmark in London ever since The Beatles decided to name their latest album according to the street where their recording studio was located???and illustrate its cover with its now-famous pedestrian crossing. However, on 1 April 2015, there was another kind of success celebrated there. A new IEEE Historic Milestone plaque has been unveiled to commemorate the numerous inventions of engineer Alan Dower Blumlein on stereo sound recording and reproduction. Reports on this event.
    IEEE Signal Processing Magazine 09/2015; 32(5):14-16. DOI:10.1109/MSP.2015.2440191
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    ABSTRACT: An article by W.L. Everitt in the 1962 50th anniversary issue of Proceedings of the IEEE, ?Engineering Education?Circa 2012 A.D.,? was one of many predictive articles that appeared in that issue [1]. One of Everitt?s observations was the distinction between training and education. He then predicted that, in the future, training will be done primarily with computers, remarking, ?Relieved of the necessity of spending most of their time on the training function, devoted teachers will be able to concentrate their efforts on ?education?.?
    IEEE Signal Processing Magazine 09/2015; 32(5):112-118. DOI:10.1109/MSP.2015.2438992
  • [Show abstract] [Hide abstract]
    ABSTRACT: Biometrics were originally developed for identification, such as for criminal investigations. More recently, biometrics have been also utilized for authentication. Most biometric authentication systems today match a user?s biometric reading against a stored reference template generated during enrollment. If the reading and the template are sufficiently close, the authentication is considered successful and the user is authorized to access protected resources. This binary matching approach has major inherent vulnerabilities.
    IEEE Signal Processing Magazine 09/2015; 32(5):42-53. DOI:10.1109/MSP.2015.2439717
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    ABSTRACT: The High Efficiency Image File Format (HEIF) is a standard developed by the Moving Picture Experts Group (MPEG) for the storage of images and image sequences. The standard facilitates file encapsulation of data coded according to the High Efficiency Video Coding (HEVC) standard. The compression performance of HEVC is superior to any alternative image or image sequence coding format. HEIF includes a rich set of features building on top of the widely used ISO Base Media File Format (ISOBMFF), making HEIF superior feature-wise compared to other image file formats. This article provides an overview of the performance, features, and design of HEIF.
    IEEE Signal Processing Magazine 07/2015; 32(4):150-156. DOI:10.1109/MSP.2015.2419292
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    ABSTRACT: Signal processing is the key to success for the efficient storage, transmission, and manipulation of information. The liveliness of signal processing relies on having a large number of students who undertake this research and embark on this career path. Signal processing is part of the curriculum in many undergraduate engineering programs. Some of the students also do their capstone or final-year projects on signal processing. To increase students? interest in signal processing and to get them to better appreciate its applications in real life, the IEEE Signal Processing Society (SPS) has created an undergraduate competition, referred to as the Signal Processing Cup (SP Cup) [1], which provides undergraduate students with an opportunity to form teams and work together to solve a challenging and interesting real-world problem using signal processing techniques and methods. The first competition was held at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2014. Based on its success, it has now been instated as an annual event at ICASSP conferences.
    IEEE Signal Processing Magazine 07/2015; 32(4):123-125. DOI:10.1109/MSP.2015.2419291