IET Signal Processing publishes research 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 by scope 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