Journal Information
Pattern Recognition Letters (PRL)
http://www.journals.elsevier.com/pattern-recognition-letters/
Impact Factor:
3.255
Publisher:
Elsevier
ISSN:
0167-8655
Viewed:
25121
Tracked:
96
Call For Papers
Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition.
Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition. Examples include:

• Statistical, structural, syntactic pattern recognition;
• Neural networks, machine learning, data mining;
• Discrete geometry, algebraic, graph-based techniques for pattern recognition;
• Signal analysis, image coding and processing, shape and texture analysis;
• Computer vision, robotics, remote sensing;
• Document processing, text and graphics recognition, digital libraries;
• Speech recognition, music analysis, multimedia systems;
• Natural language analysis, information retrieval;
• Biometrics, biomedical pattern analysis and information systems;
• Scientific, engineering, social and economical applications of pattern recognition;
• Special hardware architectures, software packages for pattern recognition.

We invite contributions as research reports or commentaries.

Research reports should be concise summaries of methodological inventions and findings, with strong potential of wide applications.
Alternatively, they can describe significant and novel applications of an established technique that are of high reference value to the same application area and other similar areas.

Commentaries can be lecture notes, subject reviews, reports on a conference, or debates on critical issues that are of wide interests.

To serve the interests of a diverse readership, the introduction should provide a concise summary of the background of the work in an accepted terminology in pattern recognition, state the unique contributions, and discuss broader impacts of the work outside the immediate subject area. All contributions are reviewed on the basis of scientific merits and breadth of potential interests.
Last updated by Dou Sun in 2021-03-20
Special Issues
Special Issue on Deep Learning for Acoustic Sensor Array Processing (DL-ASAP)
Submission Date: 2022-03-20

Acoustic sensor array processing is a well-studied field that has provided solutions to a wide range of practical problems such as source detection, estimation of source number, localization and tracking, source separation and signal enhancement, acoustic recognition, noise reduction and dereverberation. Although traditional multichannel signal processing methods reached a high level of maturity from a theoretical prospective and have shown to perform fairly well in simple applications, acoustic sensing in complex real-world applications is still a challenging problem. Reverberation, complex noise fields, dynamic reconfiguration of the acoustic scene, interferences, and concurrent multiple sources, represent today some of the most challenging problems in acoustic sensor array processing. Recently, we have witnessed a growing interest in using artificial intelligence combined with sensor arrays to potentially solve acoustic sensing problems in complex environments and in emerging applications. Learning-based methods have shown to be able to exploit the multidimensional characteristics of a sensor array and marked the way to new solutions and novel applications. The proposed special issue aims to present recent advances in the development of artificial intelligence and deep learning methods for acoustic sensor array processing emphasizing the associated theory, models, and applications. Automatic computer audition and microphone arrays need novel methods that use modern deep learning array processing addressing the challenges raised by real-life applications. The Special Issue welcomes research papers covering innovative learning-based approaches, theoretical advances, technological improvements, and novel applications in the field.
Last updated by Dou Sun in 2021-05-22
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