Aims and scope

IET Signal Processing publishes original research and review articles on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. 

Topics covered include, but are not limited to: 

    Advances in single and multi-dimensional filter design and implementation 
    Linear and nonlinear, fixed and adaptive digital filters and multirate filter banks 
    Statistical signal processing techniques and analysis 
    Classical, parametric and higher order spectral analysis 
    Signal transformation and compression techniques, including time-frequency analysis 
    System modelling and adaptive identification techniques 
    Machine learning based approaches to signal processing 
    Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques 
    Theory and application of blind and semi-blind signal separation techniques 
    Signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals 
    Direction-finding and beamforming techniques for audio and electromagnetic signals 
    Analysis techniques for biomedical signals 
    Baseband signal processing techniques for transmission and reception of communication signals 
    Signal processing techniques for data hiding and audio watermarking 
    Sparse signal processing and compressive sensing