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

Special Issue Call for Papers:

Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf

《IET 信号处理》发表有关各种信号处理和机器学习主题的研究，涵盖检测、估计、推理和分类问题中的各种应用、学科、模式和技术。发表的研究包括用于分析单维和高维数据、稀疏性、线性和非线性系统、递归和非递归数字滤波器和多速率滤波器组的算法设计进展，以及从传感器阵列处理、基于深度卷积神经网络的方法到混沌理论的应用等一系列主题。

范围涵盖的主题包括但不限于：

单维和多维滤波器设计和实现的进展

线性和非线性、固定和自适应数字滤波器和多速率滤波器组

统计信号处理技术和分析

经典、参数和高阶谱分析

信号变换和压缩技术，包括时频分析

系统建模和自适应识别技术

基于机器学习的信号处理方法

信号处理的贝叶斯方法，包括蒙特卡罗马尔可夫链和粒子滤波技术

盲和半盲信号分离技术的理论与应用

语音信号分析、增强、编码、合成和识别的信号处理技术

音频和电磁信号的测向和波束形成技术

生物医学信号的分析技术

通信信号传输和接收的基带信号处理技术

数据隐藏和音频水印的信号处理技术

稀疏信号处理和压缩感知

特刊征文：

信号处理的智能深度模糊模型 - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf

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