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Featured Article: A machine-learning phase classification scheme for anomaly detection in signals with periodic characteristics

In this paper, Lia Ahrens, Julian Ahrens and Hans D. Schotten proposes a novel approach to detecting anomalies in time series exhibiting periodic characteristics, where we applied deep convolutional neural networks for phase classification and automated phase similarity tagging. They evaluated their approach on three example datasets corresponding to the domains of cardiology, industry, and signal processing, confirming that their method is feasible in a number of contexts.



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Special Issues

Nonlinear Signal and Image Processing - A Special Issue in Honour of Giovanni L. Sicuranza on his Seventy-Fifth Birthday
Collection published: 5 August 2016

Signal Processing for Big Data
Collection published: 27 May, 2016

Recent Advances in Massive MIMO Systems
Collection published: 13 April, 2016

Recent advances on estimation and filtering in networked dynamic systems
Collection published: 11 April, 2016

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Society affiliation

The European Association for Signal Processing (EURASIP) was founded on 1 September 1978 to improve communication between groups and individuals that work within the multidisciplinary, fast growing field of signal processing in Europe and elsewhere, and to exchange and disseminate information in this field all over the world. The association exists to further the efforts of researchers by providing a learned and professional platform for dissemination and discussion of all aspects of signal processing including continuous- and discrete-time signal theory, applications of signal processing, systems and technology, speech communication, and image processing and communication.
EURASIP members are entitled to a 10% discount on the article-processing charge. To claim this discount, the corresponding author must enter the membership code when prompted. This can be requested from their EURASIP representative.

Aims and scope

The aim of the EURASIP Journal on Advances in Signal Processing is to highlight the theoretical and practical aspects of signal processing in new and emerging technologies. The journal is directed as much at the practicing engineer as at the academic researcher. Authors of articles with novel contributions to the theory and/or practice of signal processing are welcome to submit their articles for consideration. All manuscripts undergo a rigorous review process. EURASIP Journal on Advances in Signal Processing employs a paperless, electronic review process to enable a fast and speedy turnaround in the review process.

The journal is an Open Access journal since 2007.

Open special issues

Recent advances and applications of time-frequency signal analysis
Deadline for submission: 15 November 2019

Understanding and Designing Deep Neural Networks
Deadline for submission: 30 October 2019

Biomedical Informatics with Optimization and Machine Learning 2019
Deadline for submission: 31 December 2019

Call for Special Issues

The EURASIP Journal on Advances in Signal Processing (JASP) welcomes Special Issues on timely topics related to the field of signal processing at large. The objective of Special Issues is to bring together recent and high quality works in a research domain, to promote key advances in signal processing, and to provide overviews of the state-of-the-art in emerging domains, to the general signal processing audience.

Read more here

EURASIP Best paper awards 2019

We are pleased to announce that the following paper published in  EURASIP Journal on Advances in Signal Processing has been awarded a EURASIP best paper award!

Single-snapshot DOA estimation by using Compressed Sensing
Stefano Fortunati, Raffaele Grasso, Fulvio Gini, Maria S Greco and Kevin LePage

The award ceremony will take place at EUSIPCO 2019 in Spain. For more information see the EURASIP newsletter.


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​​​​​​​Open access funding and policy support by SpringerOpen​​

​​​​We offer a free open access support service to make it easier for you to discover and apply for article-processing charge (APC) funding. Learn more here