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
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    • 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
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  • Classification

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: Euclidean distance matrices (EDMs) are matrices of the squared distances between points. The definition is deceivingly simple; thanks to their many useful properties, they have found applications in psychometrics, crystallography, machine learning, wireless sensor networks, acoustics, and more. Despite the usefulness of EDMs, they seem to be insufficiently known in the signal processing community. Our goal is to rectify this mishap in a concise tutorial. We review the fundamental properties of EDMs, such as rank or (non)definiteness, and show how the various EDM properties can be used to design algorithms for completing and denoising distance data. Along the way, we demonstrate applications to microphone position calibration, ultrasound tomography, room reconstruction from echoes, and phase retrieval. By spelling out the essential algorithms, we hope to fast-track the readers in applying EDMs to their own problems. The code for all of the described algorithms and to generate the figures in the article is available online at Finally, we suggest directions for further research.
    IEEE Signal Processing Magazine 11/2015; 32(6):12-30. DOI:10.1109/MSP.2015.2398954
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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.
    IEEE Signal Processing Magazine 11/2015; 32(6):8-10. DOI:10.1109/MSP.2015.2457471

  • IEEE Signal Processing Magazine 11/2015; 32(6):6-6. DOI:10.1109/MSP.2015.2472615
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    ABSTRACT: The computational network toolkit (CNTK) is a general-purpose machine-learning tool that supports training and evaluation of arbitrary computational networks (CNs), i.e., machine-learning models that can be described as a series of computational steps. It runs under both Windows and Linux and on both central processing unit (CPU) and Compute Unified Device Architecture (CUDA)-enabled graphics processing unit (GPU) devices. The source code, periodic release builds, documents, and example setups can all be found at
    IEEE Signal Processing Magazine 11/2015; 32(6):123-126. DOI:10.1109/MSP.2015.2462371
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    ABSTRACT: In the context of singing voice synthesis, expression control manipulates a set of voice features related to a particular emotion, style, or singer. Also known as performance modeling, it has been approached from different perspectives and for different purposes, and different projects have shown a wide extent of applicability. The aim of this article is to provide an overview of approaches to expression control in singing voice synthesis. We introduce some musical applications that use singing voice synthesis techniques to justify the need for an accurate control of expression. Then, expression is defined and related to speech and instrument performance modeling. Next, we present the commonly studied set of voice parameters that can change perceptual aspects of synthesized voices. After that, we provide an up-to-date classification, comparison, and description of a selection of approaches to expression control. Then, we describe how these approaches are currently evaluated and discuss the benefits of building a common evaluation framework and adopting perceptually-motivated objective measures. Finally, we discuss the challenges that we currently foresee.
    IEEE Signal Processing Magazine 11/2015; 32(6):55-73. DOI:10.1109/MSP.2015.2424572
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    ABSTRACT: Identifying a person by his or her voice is an important human trait most take for granted in natural human-to-human interaction/communication. Speaking to someone over the telephone usually begins by identifying who is speaking and, at least in cases of familiar speakers, a subjective verification by the listener that the identity is correct and the conversation can proceed. Automatic speaker-recognition systems have emerged as an important means of verifying identity in many e-commerce applications as well as in general business interactions, forensics, and law enforcement. Human experts trained in forensic speaker recognition can perform this task even better by examining a set of acoustic, prosodic, and linguistic characteristics of speech in a general approach referred to as structured listening. Techniques in forensic speaker recognition have been developed for many years by forensic speech scientists and linguists to help reduce any potential bias or preconceived understanding as to the validity of an unknown audio sample and a reference template from a potential suspect. Experienced researchers in signal processing and machine learning continue to develop automatic algorithms to effectively perform speaker recognition?with ever-improving performance?to the point where automatic systems start to perform on par with human listeners. In this article, we review the literature on speaker recognition by machines and humans, with an emphasis on prominent speaker-modeling techniques that have emerged in the last decade for automatic systems. We discuss different aspects of automatic systems, including voice-activity detection (VAD), features, speaker models, standard evaluation data sets, and performance metrics. Human speaker recognition is discussed in two parts?the first part involves forensic speaker-recognition methods, and the second illustrates how a na?ve listener performs this task from a neuroscience perspective. We conclude this review with a comparative- study of human versus machine speaker recognition and attempt to point out strengths and weaknesses of each.
    IEEE Signal Processing Magazine 11/2015; 32(6):74-99. DOI:10.1109/MSP.2015.2462851
  • Min Wu ·

    IEEE Signal Processing Magazine 11/2015; 32(6):4-4. DOI:10.1109/MSP.2015.2468691
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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
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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
  • 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: 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
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    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