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 1558-0792
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

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this lecture note, we describe a wavelet domain denoising method consisting of making orthogonal projections of wavelet (subbands) signals of the noisy signal onto an upside down pyramid-shaped region in a multidimensional space. Each horizontal slice of the upside down pyramid is a diamond shaped region and it is called an -ball. The upside down pyramid is called the epigraph set of the -norm cost function. We show that the method leads to soft-thresholding as in standard wavelet denoising methods. Orthogonal projection operations automatically determine the soft-threshold values of the wavelet signals.
    IEEE Signal Processing Magazine 09/2015; 32(5):120-124. DOI:10.1109/MSP.2015.2440051
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    ABSTRACT: Author attribution through the recognition of visual characteristics is a commonly used approach by art experts. By studying a vast number of artworks, art experts acquire the ability to recognize the unique characteristics of artists. In this article, we present an approach that uses the same principles to discover the characteristic features that determine an artist?s touch. By training a convolutional neural network (PigeoNET) on a large collection of digitized artworks to perform the task of automatic artist attribution, the network is encouraged to discover artist-specific visual features. The trained network is shown to be capable of attributing previously unseen artworks to the actual artists with an accuracy of more than 70%. In addition, the trained network provides fine-grained information about the artist-specific characteristics of spatial regions within the artworks. We demonstrate this ability by means of a single artwork that combines characteristics of two closely collaborating artists. PigeoNET generates a visualization that indicates for each location on the artwork who is the most likely artist to have contributed to the visual characteristics at that location. We conclude that PigeoNET represents a fruitful approach for the future of computer-supported examination of artworks.
    IEEE Signal Processing Magazine 07/2015; 32(4):46-54. DOI:10.1109/MSP.2015.2406955
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    ABSTRACT: Early paper manufacturing used sieve-like molds through which paper pulp was drained. Two pieces of paper are called moldmates if they were made using the same mold. When a large body of one artist?s work on paper exists, the identification of moldmates can help in establishing chronology, suggest paper preferences, and indicate periods of intense activity of the artist. Rembrandt is an especially good example. With several thousand prints in existence today, the study of Rembrandt?s prints has occupied scholars for over two centuries, and the study of his printing papers occupies a prominent place within this scholarship [1]. This article examines the feasibility of moldmate identification via chain-line pattern matching and conducts a series of experiments that demonstrate how accurately the measurements can be made, how straight and parallel the lines may be, and provides a rule of thumb for the number of chain lines required for accurate moldmate identification using a simplified model. The problem of identifying moldmates among Rembrandt?s prints is presented as a pair of image/signal processing tasks; our strategy is to provide basic solutions to these tasks and to then reveal the shortcomings of these solutions in the hopes of encouraging future work in the signal processing community. With the support of the Morgan Library & Museum and the Metropolitan Museum of Art, both of which are in New York, we have made high-resolution data available [2] to facilitate this quest.
    IEEE Signal Processing Magazine 07/2015; 32(4):28-37. DOI:10.1109/MSP.2015.2404931
  • Source
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    ABSTRACT: Most current SAR systems offer high-resolution images featuring polarimetric, interferometric, multi-frequency, multi-angle, or multi-date information. SAR images however suffer from strong fluctuations due to the speckle phenomenon inherent to coherent imagery. Hence, all derived parameters display strong signal-dependent variance, preventing the full exploitation of such a wealth of information. Even with the abundance of despeckling techniques proposed these last three decades, there is still a pressing need for new methods that can handle this variety of SAR products and efficiently eliminate speckle without sacrificing the spatial resolution. Recently, patchbased filtering has emerged as a highly successful concept in image processing. By exploiting the redundancy between similar patches, it succeeds in suppressing most of the noise with good preservation of texture and thin structures. Extensions of patch-based methods to speckle reduction and joint exploitation of multi-channel SAR images (interferometric, polarimetric, or PolInSAR data) have led to the best denoising performance in radar imaging to date. We give a comprehensive survey of patchbased nonlocal filtering of SAR images, focusing on the two main ingredients of the methods: measuring patch similarity, and estimating the parameters of interest from a collection of similar patches.
    IEEE Signal Processing Magazine 07/2014; 31(4). DOI:10.1109/MSP.2014.2311305

  • IEEE Signal Processing Magazine 11/2013;
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    ABSTRACT: Content distribution applications such as digital broadcasting, video-on-demand services, video conferencing, surveillance, and telesurgery are confronted with difficultiesbesides the inevitable compression and quality challengeswith respect to intellectual property management, authenticity, privacy regulations, and access control. Meeting such security requirements in an end-to-end video distribution scenario poses significant challenges. If the entire content is encrypted at the content creation side, the space for signal processing operations is very limited. Decryption, followed by video processing and re-encryption is also to be avoided as it is far from efficient, complicates key management and could expose the video to possible attacks. Additionally, when the content is delivered and decrypted, the protection is gone. Watermarking can complement encryption in these scenarios by embedding a message within the content itself containing, for example, ownership information, unique buyer codes, or content descriptions. Ideally, securing the video distribution should therefore be possible throughout the distribution chain in a flexible way allowing the encryption, watermarking, and encoding/transcoding operations to commute.
    IEEE Signal Processing Magazine 03/2013; 30(2):97-107. DOI:10.1109/MSP.2012.2230220
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this work, the authors advocated for the need of social elements to make egoistic users cooperate with each other in certain situations. After discussing different forms of cooperation (specifically forced, altruistic, technology-enabled, and socially enabled), we introduced a number of example use cases. As discussed, the technology-enabled cooperation will cover a large number of these use cases. Furthermore, network coding is a technology that will make user cooperation more efficient and attractive to users. But in cases where the technology-enabled cooperation is not attractive enough, the social elements will play an important role. By means of social networks, examples were given of how social benefits can be created to persuade users to cooperate. More examples will be found in the future as social networking technology develops, but the initial examples underline the feasibility of that approach.
    IEEE Signal Processing Magazine 01/2013; 30(1). DOI:10.1109/MSP.2012.2219673
  • [Show abstract] [Hide abstract]
    ABSTRACT: If the genomic era was characterized by the massive determination of genomic sequences, the so-called postgenomic era is, among other things, characterized by a lack of methods for obtaining functionally relevant information from these raw sequences. As the number of known protein sequences grows exponentially, it is impossible to experimentally determine their biological functions and the particular regions of these proteins responsible for such functions. For this reason, computational methods that are able to process this genomic information for extracting protein functional features are sought after.
    IEEE Signal Processing Magazine 12/2012; 29(6):143. DOI:10.1109/MSP.2012.2211476