IET Signal Processing

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 implementationlinear and nonlinear, fixed and adaptive digital filters and multirate filter banksstatistical signal processing techniques and analysisclassical, parametric and higher order spectral analysissignal transformation and compression techniques, including time-frequency analysissystem modelling and adaptive identification techniquesmachine learning based approaches to signal processingBayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniquestheory and application of blind and semi-blind signal separation techniquessignal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signalsdirection-finding and beamforming techniques for audio and electromagnetic signalsanalysis techniques for biomedical signalsbaseband signal processing techniques for transmission and reception of communication signalssignal processing techniques for data hiding and audio watermarkingsparse signal processing and compressive sensingSpecial Issue Call for Papers:Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf