Artificial Intelligence Techniques for Audio, Image, and Multisensory Signal Processing

Dear Colleagues,

The rapid evolution of machine learning, deep learning, and data-driven methodologies has considerably transformed signal acquisition, analysis, and interpretation tasks in many domains. Audio and image processing, traditionally performed by means of model-based and handcrafted approaches, increasingly benefit from AI methodologies that can learn directly from nonlinear representations of complex data. Moreover, the integration of heterogeneous sensory modalities has led to new perspectives on robust perception and intelligent decision-making in real-world environments.This Special Issue offers an open forum for comprehensive presentation of novel algorithms, methodologies, and applications where AI is effectively employed to improve the performance, efficiency, or interpretability of signal processing. It aims to close the gap between theoretical developments and applied research by highlighting state-of-the-art solutions and describing current challenges and future research directions. Complementary to available reviews and application-driven studies, this Special Issue aims to emphasize cross-domain methodologies and multisensory integration and encourage interdisciplinary dialogue between the signal processing and AI research communities. We welcome original research articles and reviews on topics including, but not limited to, sound and speech analysis, music information retrieval, computer vision, medical and industrial imaging, multimodal and sensor fusion, and real-time or embedded AI systems. Emerging topics of particular interest include explainable and trustworthy AI, data scarcity, transfer learning, and edge computing. Research areas may include (but are not limited to) the following:

• Deep Learning Architectures for Audio and Speech Signal Processing
• Artificial Intelligence Techniques in Image and Video Processing
• Techniques in Multimodal and Multisensory Signal Fusion
• Sound Event Detection, Acoustic Scene Analysis, and Audio Classification
• Computer Vision and Image Understanding via AI
• Machine Learning for Music Information Retrieval and Audio Content Analysis
• Artificial Intelligence Methods for Signal Enhancement, Denoising, and Source Separation
• Explainable and Interpretable AI in Signal Processing Apps
• Edge and Embedded AI for Real-time Audio and Image Processing
• Transfer Learning and Domain Adaptation in Signal Processing Tasks
• Artificial Intelligence for Processing Biomedical and Medical Signals and Images
• Approaches to Data-Driven Environmental and Urban Signal Observation
• Self-Supervised and Unsupervised Learning for Audio and Image Signals
• AI Methods for Sensor Networks and Multisensor Data Integration
• Reliable, Trustworthy, and Ethical AI in Signal Processing Systems

We look forward to receiving your contributions.

Dr. Giuseppe Ciaburro
Guest Editor

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