期刊簡介預計審稿時間： 約9.0個月

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 信號處理》發(fā)表有關各種信號處理和機器學習主題的研究，涵蓋檢測、估計、推理和分類問題中的各種應用、學科、模式和技術。發(fā)表的研究包括用于分析單維和高維數(shù)據(jù)、稀疏性、線性和非線性系統(tǒng)、遞歸和非遞歸數(shù)字濾波器和多速率濾波器組的算法設計進展，以及從傳感器陣列處理、基于深度卷積神經網絡的方法到混沌理論的應用等一系列主題。

范圍涵蓋的主題包括但不限于：

單維和多維濾波器設計和實現(xiàn)的進展

線性和非線性、固定和自適應數(shù)字濾波器和多速率濾波器組

統(tǒng)計信號處理技術和分析

經典、參數(shù)和高階譜分析

信號變換和壓縮技術，包括時頻分析

系統(tǒng)建模和自適應識別技術

基于機器學習的信號處理方法

信號處理的貝葉斯方法，包括蒙特卡羅馬爾可夫鏈和粒子濾波技術

盲和半盲信號分離技術的理論與應用

語音信號分析、增強、編碼、合成和識別的信號處理技術

音頻和電磁信號的測向和波束形成技術

生物醫(yī)學信號的分析技術

通信信號傳輸和接收的基帶信號處理技術

數(shù)據(jù)隱藏和音頻水印的信號處理技術

稀疏信號處理和壓縮感知

特刊征文：

信號處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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