Aims & Scope

The scope of IEEE Journal of Selected Topics in Signal Processing (JSTSP) is the Field of Interest of the IEEE Signal Processing Society: “The theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques. The term “signal” includes audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and other signals.” The format of the journal allows the exploration, in depth, of a signal processing topic. This format allows the Society to not only provide issues on more mature signal processing topical areas, but also to explore new areas, particularly those at the nexus of other engineering disciplines that are dependent upon signal processing (e.g., biomedical engineering; language), as well as those not traditionally part of the engineering landscape (e.g., genetics; security; atmospheric prediction).

Additional Information

The articles in this journal are peer reviewed in accordance with the requirements set forth in the IEEE PSPB Operations Manual (sections 8.2.1.C & 8.2.2.A). Each published article was reviewed by a minimum of two independent reviewers using a single-anonymous peer review process, where the identities of the reviewers are not known to the authors, but the reviewers know the identities of the authors. Articles will be screened for plagiarism before acceptance.

Corresponding authors from low-income countries are eligible for waived or reduced open access APCs.

Special Issue on Modelling and processing language and speech in neurodegenerative disorders
提出日: 2024-11-30

The main aim of this special issue is to offer a forum that facilitates professional exchange of knowledge related to signal processing and machine learning methods for the processing, assessment and modelling of the language and the speech in neurodegenerative diseases, and presentation of the recent research advances in the field. The scope of this special issue covers many aspects of voice, speech, and language modelling and analysis, ranging from fundamental research to all kinds of advanced technologies and biomedical applications.

The scope of acceptable topics includes but is not limited to:

Topics: This special issue aims to bring together researchers and practitioners from academia and industry to share their latest findings and developments in neural speech and audio coding. The topics of interest for this special issue include but are not limited to:

    Automatic detection of articulation deficits employing the acoustic signal, palatograms, and other associated sequences
    Automatic Identification of Language Deficits in Neurodegenerative Diseases
    Automatic evaluation and differential evaluation of speech neurodegenerative disorders by using machine learning approaches
    New strategies for parameterisation and modelling the language, speech, or voice in patients with neurodegenerative diseases.
    Data augmentation techniques applied to speech in neurodegenerative diseases
    Computer-based systems for speech therapy in neurodegenerative diseases
    Technical aids and hands-free devices: vocal prostheses and aids for the disabled
    Assessment of language and articulation deficits for rehabilitation purposes
    Speech enhancement for dysarthric speech
    Multimodal analysis of language and speech in neurodegenerative diseases
    Explanatory machine learning and model interpretability in automatic detection/assessment of neurodegenerative disorders from speech
    Cross-cultural and multilingual evaluations of the language and speech of patients suffering from neurodegenerative disorders
    Interaction of speech in neurodegenerative disorders with hearing impairment
    Speech-based e-health and m-health applications applied to the monitoring of neurodegenerative disorders
    Responsible deployment of assistive technologies for automatic processing and evaluation of language and/or speech in neurodegenerative disorders

This special issue aims to offer an interdisciplinary platform for presenting new knowledge in the field. The emphases are on both basic and applied research related to the monitoring of voice, speech, and language production, as well as clinical evaluation of new developments.

Submission Guidelines: In addition to topic-specific research results, we invite high-quality submissions of tutorial or overview nature. We also welcome creative papers outside of the areas listed above but related to the overall scope of the special issue. Prospective authors can contact the Guest Editors to ascertain interest on topics that are not listed above.

Prospective authors should follow the instructions given on the IEEE JSTSP webpage, and submit their manuscript through the web submission system.

Important Dates

    Manuscript submission: 30 November 2024
    First review completed: 31 January 2025
    Revised Manuscript Due: 15 March 2025
    Second Review Completed: 15 April 2025
    Final Decision Due: 30 April 2025
    Publication: May 2025

Guest Editors

​For further information, please contact the guest editors at: 

    Stefanie Shattuck-Hufnagel, Massachusetts Institute of Technology, MA, USA
    Juan I. Godino-Llorente, Universidad Politécnica de Madrid, Madrid, Spain
    Mark Hasegawa-Johnson, University of Illinois, IL, USA
    Helen Meng, The Chinese University of Hong Kong, NT, Hong Kong
    Julián D. Arias-Londoño, Universidad de Antioquia, Colombia

Special Issue on AI in Signal & Data Science - Toward Large Language Model (LLM) Theory and Applications
提出日: 2024-12-01

To address rapidly growing interest in artificial intelligence (AI) and machine learning (ML) for signal processing and data science, the IEEE Signal Processing Society (SPS) has launched a new special series on AI in Signal & Data Science, to be published within the IEEE Journal on Selected Topic in Signal Processing (JSTSP).

The special series editorial team reserves the right to recommend submissions that are deemed out of scope or modest fit to be resubmitted to other regular SPS journals for consideration.

While manuscripts can be submitted at any time indicating for this special series, interested authors are strongly encouraged to make their submissions according to the following timetable to be considered for the 2025 issues of the first half of the year.

Important Dates

    Manuscript Submission: 1 December 2024
    First Review Due: 1 February 2025
    Revised Manuscript Due: 1 March 2025
    Second Review Due: 1 May 2025
    Final Decision Due: 15 May 2025
    Publication Date: May/June  2025

Starting in 2024, JSTSP included a series of articles on AI in Signal and Data Science. The series aims to serve  as a platform for communicating state-of-the-art AI/ML research for signal and data, highlighting the research challenges that remain unanswered and further exploring innovative principles and solutions to resolving them. Over the past year, we have received tremendous feedback from the community and have published or planned two issues featuring high-quality work on “Towards Explainable, Reliable and Sustainable Machine Learning.”

As we move forward, we continue to invite submissions of high-quality manuscripts in relevant emerging sub-topics. We seek original papers that have not been published previously and are not currently under review by any other publication venues. The initial scope of the 2025 issues in this series includes cutting-edge AI areas relevant to the broader signal and data science communities, specifically those concerning Large Language Model (LLM) based signal & data science, both in theory and applications.

This includes, but is not limited to, the following topics:

Multimodal Large Language Models: This covers advanced AI systems capable of processing, understanding, and generating content across multiple modalities, such as text, images, speech, audio, and video.

Theoretical Foundations: This area focuses on the underlying principles and methods for the designing, training, and applications of LLMs.

Training Strategies: This topic addresses the methodologies related to the training of LLMs, particularly those that tackle challenges about scalability, data and computational resource efficiency, and generalization ability. 

Fine-tuning and Adaptation: This topic explores in-weights learning methods for fine-tuning LLMs to specific tasks or domains for enhanced performance.

In-context Learning and Prompt Engineering: This subject focuses on strategies for in-context learning, where LLMs utilize contextual information to make predictions, and prompt engineering, which involves the crafting of promputs to effectively guide LMMs towards generating the desired outputs.

Reasoning Abilities: This research area analyzes the reasoning capabilities of LLMs for logic, with a focus on the Chain of Thought approach.

Causal Reasoning: This subject focuses on exploring LLM’s capabilities in causal tasks, including knowledge-based causal discovery, LLM-based causal inference, human-LLM collaboration, and understanding and improving causal reasoning.  

Explainability and Interpretability: This topic is dedicated to understanding and interpreting the outputs of LLMs to make these models transparent, interpretable, and accountable.

Emerging Applications: This topic focuses on the potential killer applications powered by LLMs, including human-machine interfaces, robotics, embodied intelligence, and others.

Special Issue on Low-Bit-Resolution Signal Processing: Algorithms, Implementations, and Applications
提出日: 2025-01-30

As the integrated circuits industry and digital signal processing (DSP) technologies continue to thrive, it is feasible to process signals with increased bandwidth and high frequency. However, full resolution processing may not be preferable or practical in some settings since it may result in huge increase in power consumption and hardware costs, especially in large-scale antenna array systems such as the multiple-input multiple output (MIMO) systems, or wireless systems using higher-frequency bands such as ultrawideband radar (UWB). Toward this end, reducing the quantization resolution at both the system level and the processing level may be necessary. Low-bit-resolution (such as 1-bit) signal processing with hardware-efficient architectures (such as low-bit ADCs and DACs) and real-time implementations (such as digital RF processing via advanced FPGA, cost-effective RFSoC design with emerging processing methods) has become a key enabling technique to balance the trade-off between performance, hardware complexity, cost, power consumption, and computation time. Although low-bit-resolution signal processing has great potential in research areas such as signal recovery and analysis, beamforming, target detection and localization, integrated sensing and communications (serving as essential parts of future wireless communication systems, radar, internet of things networks, etc.), it introduces new challenges in both understanding the theoretic limits and designing the processing schemes with low-bit-resolution solutions. This special issue aims to provide an important platform to bring together researchers from both academia and industry to introduce their most recent original works on low-bit-resolution signal processing, and catalyze new developments in this increasingly important area.

Under the constraint of low-bit-resolution signal processing, topics of interest include but are not limited to:

    Performance bound analysis
    Compressive sensing, signal recovery and analysis
    Target detection/localization, and direction of arrival estimation
    Digital beamforming
    Parameter measurement
    Precoding and decoding
    Integrated sensing and communication
    Digital automotive radar
    Artificial intelligence and neural networks
    Reconfigurable intelligent surface technology
    Multimedia processing
    High-throughput satellite communications
    Industrial IoT
    Hardware/software based real-time implementations

The Guest Editors also welcome creative papers outside the areas listed above but related to the overall scope of the special issue. Prospective authors may contact the Guest Editors to ascertain interest on topics that are not listed, and should follow the instructions given on the IEEE JSTSP webpage and submit their manuscripts via the ScholarOne submission system. All submitted manuscripts will be peer-reviewed according to the standard IEEE process. 

Important Dates

    Manuscript submission due: 30 January 2025
    First round of reviews due: 31 March 2025
    Revised manuscript due: 15 May 2025
    Second round of reviews due: 15 June 2025
    Notification of acceptance: 30 June 2025
    Publication: September 2025

Guest Editors

​For further information, please contact the guest editors at: 

    Wei Liu (Lead GE), Queen Mary University of London, UK
    Maria Sabrina Greco, University of Pisa, Italy
    Rodrigo C. de Lamare, Pontifical Catholic University of Rio de Janeiro, Brazil
    Qing Shen, Beijing Institute of Technology, China
    Mojtaba Soltanalian, University of Illinois Chicago, USA
    Pu (Perry) Wang, Mitsubishi Electric Research Laboratories (MERL), USA