Signal processing is a research area that broadly focuses on analyzing, modifying, and synthesizing signals such as sound, images, and scientific measurements. The received signals are usually disturbed by environmental or intentional interferences, e.g., noise, electricity, and occlusion.

Due to the random nature of the signal, statistical techniques play an important role in signal processing. Statistics is used in the formulation of appropriate models to describe the behaviour of the system, the development of appropriate techniques for estimation of model parameters, and the assessment of model performances. Statistical Signal Processing basically refers to the analysis of random signals using appropriate statistical techniques. Machine learning has been receiving extensive attention from both the academia and industry, and its foundation lies in statistics. For example, linear regression is a statistical technique to model the relationship between a scalar response and one or more explanatory variables. There are inextricable links between statistical signal processing and machine learning, and the synergy of between them will result in novel findings and broader applications.

This Special Issue aims to report, review, and exchange the up-to-the-date progress of the convergence of statistical signal processing and machine learning, to prompt better service provision in specific domains for a broader target audience from diverse fields. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Adaptive filter and adaptive signal processing
• Kernel methods and support vector machine
• Distributed optimization for machine learning models
• Hyperspectral image processing and bioinformatics
• Novel applications of statistical signal processing and machine learning
• New theories in statistical signal processing and machine learning
• Generative adversarial networks for signal processing
• Representation learning for statistical signal processing