AI, Machine Learning and Deep Learning in Signal Processing, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 427

Special Issue Editors


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Guest Editor
Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, 30170 Venice, Italy
Interests: computer vision; 3D reconstruction; machine learning; deep learning

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Guest Editor
Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, Republic of Korea
Interests: image/video signal processing; pattern recognition; computer vision; deep learning; artificial intelligence
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Special Issue Information

Dear Colleagues,

Recently, the entire field of signal processing has been facing new challenges and paradigm shifts due to the dramatic improvement of computational performance in hardware and an exponential increase in devices interconnected via the Internet. As a consequence, the tremendous data volume generated by such applications have to be analyzed and processed to provide useful, reliable and meaningful information.

Artificial intelligence (AI), and in particular machine (deep) learning, provides novel tools to be exploited in the field of signal processing. Consequently, new approaches, methods, theories, and tools have to be developed by the signal processing community to analyze and account for generated data volumes.

The Special Issue aims at attracting manuscripts presenting novel methods and innovative applications of AI and machine learning (including deep learning) on topics in the signal processing area. Such topics include (but are not limited to) multimedia systems, audio and video processing, and augmented and virtual reality. The objective of the Special Issue is to bring together recent high-quality works in AI to promote key advances in signal processing areas covered by the journal and to provide reviews of the state of the art in these emerging domains.

Dr. Mara Pistellato
Prof. Dr. Byung-Gyu Kim
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence (AI)
  • deep learning
  • machine learning
  • signal processing
  • image and video processing
  • audio and acoustic signal processing
  • biomedical signal processing
  • speech processing
  • multimedia signal processing
  • multidimensional signal processing
  • augmented reality
  • virtual reality

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Published Papers (1 paper)

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Research

15 pages, 717 KiB  
Article
Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
by Yuting Yan and Qinghua Huang
Appl. Sci. 2024, 14(11), 4488; https://doi.org/10.3390/app14114488 - 24 May 2024
Viewed by 192
Abstract
To overcome the limitations of traditional methods in reverberant and noisy environments, a robust multi-scale fusion neural network with attention mask is designed to improve direction-of-arrival (DOA) estimation accuracy for acoustic sources. It combines the benefits of deep learning and complex-valued operations to [...] Read more.
To overcome the limitations of traditional methods in reverberant and noisy environments, a robust multi-scale fusion neural network with attention mask is designed to improve direction-of-arrival (DOA) estimation accuracy for acoustic sources. It combines the benefits of deep learning and complex-valued operations to effectively deal with the interference of reverberation and noise in speech signals. The unique properties of complex-valued signals are exploited to fully capture inherent features and rich information is preserved in the complex field. An attention mask module is designed to generate distinct masks for selectively focusing and masking based on the input. After that, the multi-scale fusion block efficiently captures multi-scale spatial features by stacking complex-valued convolutional layers with small size kernels, and reduces the module complexity through special branching operations. Experimental results demonstrate that the model achieves significant improvements over other methods for speaker localization in reverberant and noisy environments. It provides a new solution for DOA estimation for acoustic sources in different scenarios, which has significant theoretical and practical implications. Full article
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