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IEEE Signal Processing Magazine is a peer-reviewed journal published by IEEE, covering Diverse Scientific and Economic Studies, Human auditory perception and evaluation, Neural Networks and Applications. Over its lifetime it has published 3,930 papers, which have received 329,090 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Signal Processing Magazine, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Signal Processing Magazine style. No sign-up. |
| Publishes research in | Diverse Scientific and Economic Studies Human auditory perception and evaluation Neural Networks and Applications Blind Source Separation Techniques Image and Signal Denoising Methods |
| ISSN | 1053-5888 |
| Citation impact (2-yr) | 2.3 |
| h-index | 257 |
| i10-index | 1,527 |
| Top institutions publishing here | Signal Processing (United States) |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit…
Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist…
Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we…
In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research.
The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A…
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