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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

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