IEEE SIGNAL PROCESSING MAGAZINEIEEE信号处理杂志
IEEE SIGNAL PROCESSING MAGAZINE(英文缩写 IEEE SIGNAL PROC MAG),ISSN 1053-5888,eISSN 1558-0792,中文译名:IEEE信号处理杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 15.204 | Q1 |
| 2022 | 14.900 | Q1 |
| 2023 | 9.400 | Q1 |
| 2024 | 9.600 | Q1 |
| 2025 | 10.800 | Q1 |
IEEE SIGNAL PROCESSING MAGAZINE 最新收录文献
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1. Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities.
PMID:日期:2026-01-26Naturalistic recordings capture audio in real-world environments where participants behave naturally without interference from researchers or experimental protocols. Naturalistic long-form recordings extend this concept by capturing spontaneous and continuous interactions over extended periods, often spanning hours or even days, in participants' daily lives. Naturalistic recordings have been extensively used to study children's behaviors, including how they interact with others in their environment, in the fields of psychology, education, cognitive science, and clinical research. These recordings provide an unobtrusive way to observe children in real-world settings beyond controlled and constrained experimental environments. Advancements in speech technology and machine learning have provided an initial step for researchers to automatically and systematically analyze large-scale naturalistic recordings of children. Despite the imperfect accuracy of machine learning models, these tools still offer valuable opportunities to uncover important insights into children's cognitive and social development. Several critical speech technologies involved include speaker diarization, vocalization classification, word count estimate from adults, speaker verification, and language diarization for code-switching. Most of these technologies have been primarily developed for adults, and speech technologies applied to children specifically are still vastly under-explored. To fill this gap, we discuss current progress, challenges, and opportunities in advancing these technologies to analyze naturalistic recordings of children during early development (< 3 years of age). We strive to inspire the signal processing community and foster interdisciplinary collaborations to further develop this emerging technology and address its unique challenges and opportunities.
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2. The Marriage of Neurotechnologies and Artificial Intelligence: Ethical, regulatory, and technological aspects.
2. 神经技术与人工智能的融合：伦理、监管与技术层面PMID:日期:2025-12-22The dual concepts of neurotechnology and artificial intelligence (AI) form an intriguing but also potentially explosive mixture because of its many ethical and legal implications. The advent of AI and the progress in neurotechnologies are reshaping the landscape not only in all scientific fields but also in everyday life both individually and collectively, ushering in a new era where the centrality, integrity and identity of humans is no longer a fact. Such tumultuous progress has implications at all levels, individual, societal, economical and political. Without the pretension of exploring the whole set of relevant aspects, we aim at providing a multi-disciplinary view on the main ethical, legal and societal issues stemming from neurotechnology and AI, by assessing them using keywords like trustworthiness, fairness, awareness, security, and privacy. In this paper, we propose an overview on the current scenario, taking a philosophical perspective in the light of ethics, and boiling it down to aspects closely related to the technological developments and the regulatory measures that are currently in-place and called for.
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3. Multimodal data fusion in neuroscience: promises, challenges and future directions.
PMID:日期:2025-12-19Multimodal fusion provides significant benefits over single modality analysis by leveraging both shared and complementary information across diverse data sources. In this article, we systematically review methods for fusion of heterogonous multimodal biomedical data of varying dimensionality (including neuroimaging, biomics, clinical phenotypes and text), with a focus on neuroscience. We discuss the strengths and limitations of these strategies based on a survey of 302 research articles. Next, we examine the applications of these methods to a variety of scenarios spanning a continuum from scientific research to clinical practice. Finally, an in-depth discussion of common challenges and promising directions for future development of multimodal biomedical data fusion are provided. Overall, multimodal fusion shows substantial benefits and transformative potential in the field of neuroscience. Future research should prioritize improving model generalization, enhancing interpretability, addressing inherent data limitations, and developing unified platforms alongside multimodal foundational models to bridge the gap between fusion techniques, research, and application to various domains.
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4. Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits.
PMID:日期:2025-11-24Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizability. This challenge is particularly acute in magnetic resonance imaging (MRI), where image appearance varies widely across pulse sequences and scanner hardware. A recent domain-randomization strategy addresses the generalization problem by training deep neural networks on synthetic images with randomized intensities and anatomical content. By generating diverse data from anatomical segmentation maps, the approach enables models to accurately process image types unseen during training, without retraining or fine-tuning. It has demonstrated effectiveness across modalities including MRI, computed tomography, positron emission tomography, and optical coherence tomography, as well as beyond neuroimaging in ultrasound, electron and fluorescence microscopy, and X-ray microtomography. This tutorial paper reviews the principles, implementation, and potential of the synthesis-driven training paradigm. It highlights key benefits, such as improved generalization and resistance to overfitting, while discussing trade-offs such as increased computational demands. Finally, the article explores practical considerations for adopting the technique, aiming to accelerate the development of generalizable tools that make deep learning more accessible to domain experts without extensive computational resources or machine learning knowledge.
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5. Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning.
5. 源自生成式人工智能与深度表征学习的新兴脑到内容技术PMID:日期:2025-01-01Rapid advances in generative artificial intelligence (AI) and deep representation learning have revolutionized numerous engineering applications in signal processing, computer vision, speech recognition and translation, and natural language processing due to amazingly powerful representation power (e.g., [1,2]). Generative AI-empowered tools, such as ChatGPT and Sora, have fundamentally changed the landscape of human-computer communications research. One emerging application along this line is to link the brain to the computer (i.e., brain-computer interface or BCI) and to develop paradigm-shift brain-to-content technologies. This BCI system upgrade (i.e., BCI 2.0) is empowered by generative AI and deep learning ("new engine") and large amounts of data ("gas"). In this article, we will revisit the old song sung in a new tune, highlight some state-of-the-art progresses, and briefly discuss the future outlook.
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6. Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey.
6. 低剂量计算机断层扫描的基于物理/模型和数据驱动方法：综述PMID:日期:2023-02-27Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black box nature and major issues such as instabilities, which is a major barrier to apply deep learning methods in low-dose CT applications. An emerging trend is to integrate imaging physics and model into deep networks, enabling a hybridization of physics/model-based and data-driven elements. In this paper, we systematically review the physics/model-based data-driven methods for LDCT, summarize the loss functions and training strategies, evaluate the performance of different methods, and discuss relevant issues and future directions.
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7. High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions.
7. 基于物理建模与数据驱动机器学习相结合的高维MR空间光谱成像：当前进展与未来方向PMID:日期:2023-02-27Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical challenges due to high dimensionality and low signal-to-noise ratio (SNR). Recent technological developments integrating physics-based modeling and data-driven machine learning that exploit unique physical and mathematical properties of MRSI signals have demonstrated impressive performance in addressing these challenges for rapid, high-resolution, quantitative MRSI. This paper provides a systematic review of these progresses in the context of MRSI physics and offers perspectives on promising future directions.
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8. Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging.
8. 计算磁共振成像的物理驱动深度学习：结合物理和机器学习改进医学成像PMID:日期:2023-01-02Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction. We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these. We then focus on physics-driven deep learning approaches, covering physics-driven loss functions, plug-and-play methods, generative models, and unrolled networks. We highlight domain-specific challenges such as real- and complex-valued building blocks of neural networks, and translational applications in MRI with linear and non-linear forward models. Finally, we discuss common issues and open challenges, and draw connections to the importance of physics-driven learning when combined with other downstream tasks in the medical imaging pipeline.
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9. Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging.
9. 解读大脑生物标志物：解读基于机器学习的预测性神经影像所面临的挑战与解决方案PMID:日期:2022-06-28Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.
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10. Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness.
10. 矩阵与张量分解中的可复现性：聚焦模型匹配、可解释性与唯一性PMID:日期:2022-06-28该文献暂无摘要。