Signal Processing Based on Machine Learning Techniques

Dear Colleagues,

Machine learning (ML) has recently attracted a great deal of attention in the area of signal processing due to its intrinsic ability to analyze the signal in both the time and frequency domains.

Machine learning algorithms are used in many fields, especially in pattern recognition, signal classification, signal processing, computer vision, and biomedical technologies.

In recent years, research on signal processing has extended towards the use of artificial intelligence techniques and, in particular, towards recent machine learning techniques, which include the modern technologies of CNNs (convolutional neural networks) and DNNs (deep neural networks). The main advantages concern their greater accuracy in performance, in terms of robustness to signal degradation, and their lower computational complexity as a result of the possibility of processing data directly in the time domain without necessarily having to implement sets of features, which are typically obtained in the frequency domain. The application fields are numerous: speech recognition and identification, speech synthesis, classification of signals (image, speech, audio, and medical), recognition of emotions, automatic diagnosis, advanced methods and algorithms in smart sensors.

This Special Issue is devoted to reporting novel scientific ideas, approaches, results, and (prototype) solutions/applications on signal processing algorithms based on machine learning. Contributions are solicited in the wide spectrum of topics listed below.

• Digital signal processing based on machine learning;
• Signal processing algorithms and neural networks;
• Artificial intelligence for multimedia signal processing;
• Signal detection using machine learning;
• CNNs and DNNs for signal classification and coding;
• Application of CNNs to the diagnosis of biomedical signals;
• Audio forensics analysis based on machine learning;
• Video signal processing and CNNs;
• Computer vision based on CNNs;
• Pattern recognition and machine learning;
• Rainfall estimation using a convolutional neural network.

Dr. Roberta Avanzato
Dr. Francesco Beritelli
Guest Editors

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