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: 4.48

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 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 6.90
Cited half-life 6.30
Immediacy index 0.22
Eigenfactor 0.02
Article influence 3.56
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
    • Author's pre-print must be removed upon publication of final version and replaced with either full citation to IEEE work with a Digital Object Identifier or link to article abstract in IEEE Xplore or replaced with Authors post-print
    • 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: The author announces the creation of the Signal Processing Repository (SigPort), an online repository of manuscripts, reports, technical white papers, theses, and supporting materials. Created and supported by the IEEE Signal Processing Society (SPS), SigPort collects technical material of interest to the broad signal and information processing community, with categories covering each of the Society's technical committees. Much like arXiv, SigPort hosts material to help individuals obtain early and broad exposure to their work. SigPort provides a time stamp for each uploaded document; a unique URL is assigned to the document, designating it as part of the IEEE SPS SigPort as well as for easy referencing. Also similar to arXiv, SigPort papers are not peer reviewed. Authors retain all the rights to their documents and can submit them later to journals, conferences, books, etc., since submissions to the SigPort repository are not as restricted as formal publications. We expect a majority of the e-prints to be submitted to one of the Society's journals for publication, but some works may remain purely as eprints and will never be published in a peer-reviewed journal. SigPort documents can be accessed for free at
    IEEE Signal Processing Magazine 07/2015; 32(4):6-6. DOI:10.1109/MSP.2015.2425152
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    ABSTRACT: Discrete orthogonal transforms such as the discrete Fourier transform (DFT), discrete Hartley transform (DHT), and Walsh?Hadamard transform (WHT) play important roles in the fields of digital signal processing, filtering, and communications. In recent years, there has been a growing interest in the sliding transform process where the transform window is shifted one sample at a time and the transform process is repeated.
    IEEE Signal Processing Magazine 07/2015; 32(4):145-156. DOI:10.1109/MSP.2015.2412144
  • IEEE Signal Processing Magazine 07/2015; 32(4):4-4. DOI:10.1109/MSP.2015.2425151
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    ABSTRACT: Texture characterization of photographic prints can provide scholars with valuable information regarding photographers? aesthetic intentions and working practices. Currently, texture assessment is strictly based on the visual acuity of a range of scholars associated with collecting institutions, such as museum curators and conservators. Natural interindividual discrepancies, intraindividual variability, and the large size of collections present a pressing need for computerized and automated solutions for the texture characterization and classification of photographic prints. In the this article, this challenging image processing task is addressed using an anisotropic multiscale representation of texture, the hyperbolic wavelet transform (HWT), from which robust multiscale features are constructed. Cepstral distances aimed at ensuring balanced multiscale contributions are computed between pairs of images. The resulting large-size affinity matrix is then clustered using spectral clustering, followed by a Ward linkage procedure. For proof of concept, these procedures are first applied to a reference data set of historic photographic papers that combine several levels of similarity and second to a large data set of culturally valuable photographic prints held by the Museum of Modern Art in New York. The characterization and clustering results are interpreted in collaboration with art scholars with an aim toward developing new modes of art historical research and humanities-based collaboration.
    IEEE Signal Processing Magazine 07/2015; 32(4):18-27. DOI:10.1109/MSP.2015.2402056
  • IEEE Signal Processing Magazine 07/2015; 32(4):138-144. DOI:10.1109/MSP.2015.2405752
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    ABSTRACT: If you are worried that artificial intelligence enabled systems are well on their way toward assuming total command of the planet, you can take some heart in the fact that there is still a great deal of important research being done in human???machine interfaces (HMIs), much of it involving signal processing. Making certain that various types of systems do precisely what their human masters demand lies at the heart of most HMI research. The current HMI field is very competitive, and academic, government, and commercial researchers are working hard to create advanced technologies that are both useful and marketable. The major trends driving the sector include an ever-increasing demand for enhanced user efficiency; rapid growth in information technology and telecom sectors; and a continuing expansion of electronic, mobile, computer, and electromechanical applications.
    IEEE Signal Processing Magazine 07/2015; 32(4):8-11. DOI:10.1109/MSP.2015.2412128
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    ABSTRACT: Hanging in the Saint Bavo Cathedral in Ghent, Belgium, is The Ghent Altarpiece, also known as The Adoration of the Mystic Lamb (see Figure 1). According to an inscription on the outer frames, it was painted by brothers Hubert and Jan van Eyck for Joos Vijd and?his wife Elisabeth Borluut in 1432. It is one of the most admired and influential paintings in the history of art and has given rise to many intriguing questions that have been puzzling art historians to date [11]. Moreover, the material history of the panels is very complicated. They were hidden, dismantled, moved away, stolen, and recovered during riots, fires and wars. The recovery of the panels by the U.S. Army in the Nazi hoards deep in the Altaussee salt mines has particularly marked memories. One panel was stolen in 1934 and never recovered. Besides varying conservation conditions, the panels underwent numerous restoration treatments and were even partially painted over.
    IEEE Signal Processing Magazine 07/2015; 32(4):112-122. DOI:10.1109/MSP.2015.2411753
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    ABSTRACT: Canvas analysis is an important tool in art-historical studies, as it can provide information on whether two paintings were made on canvas that originated from the same bolt. Canvas analysis algorithms analyze radiographs of paintings to identify (ir)regularities in the spacings between the canvas threads. To reduce noise, current state-of-the-art algorithms do this by averaging the signal over a number of threads, which leads to information loss in the final measurements. This article presents an algorithm capable of performing thread-level canvas analysis: the algorithm identifies each of the individual threads in the canvas radiograph and directly measures between-distances and angles of the identified threads. We present two case studies to illustrate the potential merits of our thread-level canvas analysis algorithm, viz. on a small collection of paintings ostensibly by Nicholas Poussin and on a small collection of paintings by Vincent van Gogh.
    IEEE Signal Processing Magazine 07/2015; 32(4):38-45. DOI:10.1109/MSP.2015.2407091
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    ABSTRACT: Cultural heritage collections are being digitized and made available through online tools. Due to the large volume of documents being digitized, not enough manpower is available to provide useful annotations. Is this article, we discuss the use of automatic tools to both automatically index the documents (i.e., provide labels) and search through the collections. We detail the challenges specific to these collections as well as research directions that must be followed to answer the questions raised by these new data.
    IEEE Signal Processing Magazine 07/2015; 32(4):95-102. DOI:10.1109/MSP.2015.2409557
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    ABSTRACT: We propose a holistic system to classify ancient Roman Republican coins based on their reverse-side motifs. The bag-of-visual-words (BoW) model is enriched with spatial information to increase the discriminative power of the coin image representation. This is achieved by combining a spatial pooling scheme with co-occurrence encoding of visual words. We specifically address the required geometric invariance properties of image-based ancient coin classification, as coins from different collections can be located at differing image locations, have various scales in the images and can undergo various in-plane rotations. We evaluate our method on a data set of 2,224 coin images from three different sources. The experimental results show that our proposed image representation is more discriminative than the traditional bag-of-visual-words model while still being invariant to the mentioned geometric transformations. For 29 motifs, the system achieves a classification rate of 82%. It is considered to act as a helpful tool for numismatists in the near future, which facilitates and supports the traditional coin classification process by a faster presorting of coins.
    IEEE Signal Processing Magazine 07/2015; 32(4):64-74. DOI:10.1109/MSP.2015.2409331
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    ABSTRACT: Accurate measurements of precipitation are essential for many applications, ranging from flash-flood warnings to water resource management. However, the accuracy of the existing tools is limited by various technical and practical reasons. Percipitation monitoring has traditionally been known to rely on gauges, weather radars, and satellites. Recently, a new approach has begun to be examined, the usage of commercial wireless communication networks (CWCNs), which enjoys the lack of any need for deployment procedures or costs, and which is already widely spread across countries.
    IEEE Signal Processing Magazine 05/2015; 32(3):110-122. DOI:10.1109/MSP.2014.2309705
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    ABSTRACT: Sensors and other data sources, combined with sophisticated signal processing techniques, promise to help scientists better observe and analyze various types of environmental data.
    IEEE Signal Processing Magazine 05/2015; 32(3):13-161. DOI:10.1109/MSP.2015.2393931
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    ABSTRACT: Anyone who has served as a technical program committee (TPC) chair for a conference (or program manager for a funding agency) understands that paper (or proposal panel) review assignment is a demanding job that takes a lot of time, and reviewers are rarely satisfied with the end results. This article presents signal processing tools for two critical ?mass assignment? tasks: assigning papers (or proposals) to reviewers in a way that matches reviewing expertise to scientific content while respecting the reviewers? capacity constraints and splitting accepted papers (or submitted proposals) to sessions (panels) while adhering to session (panel) capacity constraints. The basic idea is to use feature vectors to represent papers and reviewers. Features can be key words or phrases (e.g., optimization or sensor networks) or other types of attributes (e.g., timeliness). This viewpoint enables optimal assignment problem formulations that make sense from a scientific and practical point of view. While optimal solutions are hard to compute for a large number of papers and reviewers, high-quality approximate solutions of moderate complexity are developed here using familiar signal processing and optimization tools. These algorithmic solutions easily outperform days of expert manual work as demonstrated in experiments with real conference data.
    IEEE Signal Processing Magazine 05/2015; 32(3):141-155. DOI:10.1109/MSP.2014.2359230
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    ABSTRACT: In this article, we present an account of the state of the art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different implementations of its components. We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques. The data set recorded for this purpose is presented along with the performance metrics that are used to evaluate the algorithms and statistical significance tests to compare the submitted methods.
    IEEE Signal Processing Magazine 05/2015; 32(3):16-34. DOI:10.1109/MSP.2014.2326181
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    ABSTRACT: Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are the two most common types of acoustic models used in statistical parametric approaches for generating low-level speech waveforms from high-level symbolic inputs via intermediate acoustic feature sequences. However, these models have their limitations in representing complex, nonlinear relationships between the speech generation inputs and the acoustic features. Inspired by the intrinsically hierarchical process of human speech production and by the successful application of deep neural networks (DNNs) to automatic speech recognition (ASR), deep learning techniques have also been applied successfully to speech generation, as reported in recent literature.
    IEEE Signal Processing Magazine 05/2015; 32(3):35-52. DOI:10.1109/MSP.2014.2359987
  • IEEE Signal Processing Magazine 05/2015; 32(3):6-6. DOI:10.1109/MSP.2015.2404372