Pub Date : 2026-01-29DOI: 10.1109/MSP.2026.3659283
{"title":"2025 Index Signal Processing Magazine","authors":"","doi":"10.1109/MSP.2026.3659283","DOIUrl":"https://doi.org/10.1109/MSP.2026.3659283","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 6","pages":"121-136"},"PeriodicalIF":9.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/MSP.2025.3581871
Andres Kwasinski;Marios S. Pattichis;Alan Bovik;Edward J. Delp;Aggelos K. Katsaggelos;Anna Scaglione;Sharon Gannot;Andreas Spanias;Gene Cheung;Martin Haardt;José M. F. Moura
Recently, the Signal Processing Society Education Board launched an initiative to host roundtables discussing the impact of machine learning and artificial intelligence advancements in signal processing education. The first of these events took place in October 2023. The panelists were Profs. Alan Bovik (The University of Texas at Austin), Edward J. Delp (Purdue University), Aggelos K. Katsaggelos (Northwestern University), Anna Scaglione (Cornell University and Cornell Tech.), Sharon Gannot (Bar-Ilan University), and Andreas Spanias (Arizona State University). The panel was moderated by Profs. Marios Pattichis (University of New Mexico) and Andres Kwasinski (Rochester Institute of Technology). The second panel, which was organized during ICASSP 2024, had as panelists Profs. Gene Cheung (York University), Danilo Mandic (Imperial College London), Martin Haardt (Ilmenau University of Technology), and José M. F. Moura (Carnegie Mellon University). This panel was moderated by Prof. Andres Kwasinski. This article summarizes the roundtable discussions, distills key lessons, and offers additional insights. A key consensus among the panels was that we are at a pivotal moment when we are witnessing the emergence of a new discipline that combines the model-based approach from traditional signal processing with the data-driven approach from ML and Data Science. The emergence of this new discipline calls for new pedagogical methods and brings new tools that will reshape how we do research.
最近，信号处理学会教育委员会发起了一项倡议，举办圆桌会议，讨论机器学习和人工智能进步对信号处理教育的影响。这些事件中的第一次发生在2023年10月。小组成员都是教授。Alan Bovik（德克萨斯大学奥斯汀分校），Edward J. Delp（普渡大学），Aggelos K. Katsaggelos（西北大学），Anna Scaglione（康奈尔大学和康奈尔理工大学），Sharon Gannot（巴伊兰大学）和Andreas Spanias（亚利桑那州立大学）。该小组由教授们主持。Marios Pattichis（新墨西哥大学）和Andres Kwasinski（罗切斯特理工学院）。第二个小组是在ICASSP 2024期间组织的，小组成员是教授。Gene b张（约克大学），Danilo Mandic（伦敦帝国理工学院），Martin Haardt（伊尔梅诺理工大学）和josise M. F. Moura（卡内基梅隆大学）。本次座谈由andreskwasinski教授主持。本文总结了圆桌会议的讨论，提炼了关键的经验教训，并提供了额外的见解。小组之间的一个关键共识是，我们正处于一个关键时刻，我们正在见证一门新学科的出现，该学科将传统信号处理的基于模型的方法与ML和数据科学的数据驱动方法相结合。这门新学科的出现需要新的教学方法，并带来新的工具，这些工具将重塑我们的研究方式。
{"title":"Lessons From Two Roundtables on Artificial Intelligence and Signal Processing Education: Addressing the emergence of a new era and a new discipline [Special Issue on Artificial Intelligence for Education: A Signal Processing Perspective]","authors":"Andres Kwasinski;Marios S. Pattichis;Alan Bovik;Edward J. Delp;Aggelos K. Katsaggelos;Anna Scaglione;Sharon Gannot;Andreas Spanias;Gene Cheung;Martin Haardt;José M. F. Moura","doi":"10.1109/MSP.2025.3581871","DOIUrl":"10.1109/MSP.2025.3581871","url":null,"abstract":"Recently, the Signal Processing Society Education Board launched an initiative to host roundtables discussing the impact of machine learning and artificial intelligence advancements in signal processing education. The first of these events took place in October 2023. The panelists were Profs. Alan Bovik (The University of Texas at Austin), Edward J. Delp (Purdue University), Aggelos K. Katsaggelos (Northwestern University), Anna Scaglione (Cornell University and Cornell Tech.), Sharon Gannot (Bar-Ilan University), and Andreas Spanias (Arizona State University). The panel was moderated by Profs. Marios Pattichis (University of New Mexico) and Andres Kwasinski (Rochester Institute of Technology). The second panel, which was organized during ICASSP 2024, had as panelists Profs. Gene Cheung (York University), Danilo Mandic (Imperial College London), Martin Haardt (Ilmenau University of Technology), and José M. F. Moura (Carnegie Mellon University). This panel was moderated by Prof. Andres Kwasinski. This article summarizes the roundtable discussions, distills key lessons, and offers additional insights. A key consensus among the panels was that we are at a pivotal moment when we are witnessing the emergence of a new discipline that combines the model-based approach from traditional signal processing with the data-driven approach from ML and Data Science. The emergence of this new discipline calls for new pedagogical methods and brings new tools that will reshape how we do research.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 6","pages":"39-50"},"PeriodicalIF":9.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/MSP.2025.3594309
Shen Wang;Tianlong Xu;Hang Li;Chaoli Zhang;Joleen Liang;Jiliang Tang;Philip S. Yu;Qingsong Wen
The advent of large language models (LLMs) has ushered in a new era of possibilities in the realm of education. This survey article summarizes recent progress in the application of LLMs in educational settings from multiple perspectives, including student and teacher assistance, adaptive learning, and commercial tools. Additionally, it systematically reviews technological advancements in each area, compiles related datasets and benchmarks, and identifies the risks and challenges associated with deploying LLMs in education. Furthermore, the article outlines future research opportunities, highlighting promising directions. This article aims to provide a comprehensive technological overview for educators, researchers, and policy makers to harness the power of LLMs, revolutionize educational practices, and foster a more effective personalized learning environment.
{"title":"Large Language Models for Education: A survey and outlook","authors":"Shen Wang;Tianlong Xu;Hang Li;Chaoli Zhang;Joleen Liang;Jiliang Tang;Philip S. Yu;Qingsong Wen","doi":"10.1109/MSP.2025.3594309","DOIUrl":"10.1109/MSP.2025.3594309","url":null,"abstract":"The advent of large language models (LLMs) has ushered in a new era of possibilities in the realm of education. This survey article summarizes recent progress in the application of LLMs in educational settings from multiple perspectives, including student and teacher assistance, adaptive learning, and commercial tools. Additionally, it systematically reviews technological advancements in each area, compiles related datasets and benchmarks, and identifies the risks and challenges associated with deploying LLMs in education. Furthermore, the article outlines future research opportunities, highlighting promising directions. This article aims to provide a comprehensive technological overview for educators, researchers, and policy makers to harness the power of LLMs, revolutionize educational practices, and foster a more effective personalized learning environment.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 6","pages":"51-63"},"PeriodicalIF":9.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1109/MSP.2025.3648212
{"title":"Call For Papers Special Issue","authors":"","doi":"10.1109/MSP.2025.3648212","DOIUrl":"https://doi.org/10.1109/MSP.2025.3648212","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 6","pages":"7-7"},"PeriodicalIF":9.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11364203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1109/MSP.2025.3610974
Jialu Li;Marvin Lavechin;Xulin Fan;Nancy L. McElwain;Alejandrina Cristia;Paola Garcia-Perera;Mark A. Hasegawa-Johnson
Naturalistic 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 (ML) have provided an initial step for researchers to automatically and systematically analyze large-scale naturalistic recordings of children. Despite the imperfect accuracy of ML 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 underexplored. 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.
{"title":"Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, challenges, and opportunities","authors":"Jialu Li;Marvin Lavechin;Xulin Fan;Nancy L. McElwain;Alejandrina Cristia;Paola Garcia-Perera;Mark A. Hasegawa-Johnson","doi":"10.1109/MSP.2025.3610974","DOIUrl":"https://doi.org/10.1109/MSP.2025.3610974","url":null,"abstract":"Naturalistic 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 (ML) have provided an initial step for researchers to automatically and systematically analyze large-scale naturalistic recordings of children. Despite the imperfect accuracy of ML 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 underexplored. 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.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 6","pages":"16-34"},"PeriodicalIF":9.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1109/MSP.2026.3652391
Kenneth Lam
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
提供社会信息，可能包括新闻，评论或技术笔记，从业者和研究人员应该感兴趣。
{"title":"Bangalore, Czechoslovakia, and Madras Chapters Receive the 2025 Chapter of the Year Award! [Society News]","authors":"Kenneth Lam","doi":"10.1109/MSP.2026.3652391","DOIUrl":"https://doi.org/10.1109/MSP.2026.3652391","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 6","pages":"6-6"},"PeriodicalIF":9.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11364184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gender bias refers to systematic and unequal treatment based on an individual’s gender [1], or the preference or prejudice toward one gender over another [2]. Gender biases can be led by humans or autonomous systems, and although their occurrence in decision making is usually unintentional, they have profound consequences on societal interactions. These biases are particularly detrimental in educational settings, where they can reinforce stereotypes, influence student performance and engagement, and perpetuate systemic inequalities. In this regard, a source of concern is artificial intelligence (AI) systems and machine learning (ML) models that use natural language processing (NLP) techniques as they convey challenges and opportunities. Although using gender-biased datasets on model training has known harmful consequences [3], AI/ML’s unparalleled ability for text processing makes them an attractive resource for detecting and mitigating gender bias from natural language. Our focus is to address gender bias in educational contexts using AI, a crucial step to fostering inclusive learning environments and promoting equitable opportunities for all learners.
{"title":"Using Language Models to Detect and Reduce Gender Bias in University Forum Messages","authors":"Gianina Salomó-López;Cristóbal Alcázar;Roberto Barceló;Camilo Carvajal Reyes;Darinka Radovic;Felipe Tobar","doi":"10.1109/MSP.2025.3600847","DOIUrl":"https://doi.org/10.1109/MSP.2025.3600847","url":null,"abstract":"Gender bias refers to systematic and unequal treatment based on an individual’s gender <xref>[1]</xref>, or the preference or prejudice toward one gender over another <xref>[2]</xref>. Gender biases can be led by humans or autonomous systems, and although their occurrence in decision making is usually unintentional, they have profound consequences on societal interactions. These biases are particularly detrimental in educational settings, where they can reinforce stereotypes, influence student performance and engagement, and perpetuate systemic inequalities. In this regard, a source of concern is artificial intelligence (AI) systems and machine learning (ML) models that use natural language processing (NLP) techniques as they convey challenges and opportunities. Although using gender-biased datasets on model training has known harmful consequences <xref>[3]</xref>, AI/ML’s unparalleled ability for text processing makes them an attractive resource for detecting and mitigating gender bias from natural language. Our focus is to address gender bias in educational contexts using AI, a crucial step to fostering inclusive learning environments and promoting equitable opportunities for all learners.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 6","pages":"95-109"},"PeriodicalIF":9.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1109/MSP.2026.3652290
Tülay Adali;Xiang Bai;Christoph Busch;Stefano Buzzi;Liqun Chen;Yiqiang Chen;Xun Chen;Zoran D. Cvetkovic;Rodrigo C. DeLamare;Fang Deng;Kutluyil Dogancay;Marek Domanski;Yuming Fang;Gang Feng;Mustafa C. Gursoy;Yu-Wen Huang;Ioannis Katsavounidis;Usman A. Khan;Ercan Kuruoglu;Chiman Kwan;Lifeng Lai;Daniel L. Lau;Xiaohui Liang;Joseph C. Liberti;Liang Liu;Durga P. Malladi;C. Mecklenbraeuker;Joseph A. Paradiso;Vishal M. Patel;Yuxin Peng;Boaz Rafaely;Alejandro R. Ribeiro;Yong Man Ro;Saeed Sanei;George A. Saon;Stephan Schlamminger;Farhana Sheikh;Chao Shen;Brian Telfer;Wenwu Wang;Jing Yang;Yi Yang;Kai Yu;Wei Yu;Xiaojun Yuan;Jun Zhou;Asli Celikyilmaz;Haohong Wang
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
提供社会信息，可能包括新闻，评论或技术笔记，从业者和研究人员应该感兴趣。
{"title":"45 SPS Members Elevated to Fellow [Society News]","authors":"Tülay Adali;Xiang Bai;Christoph Busch;Stefano Buzzi;Liqun Chen;Yiqiang Chen;Xun Chen;Zoran D. Cvetkovic;Rodrigo C. DeLamare;Fang Deng;Kutluyil Dogancay;Marek Domanski;Yuming Fang;Gang Feng;Mustafa C. Gursoy;Yu-Wen Huang;Ioannis Katsavounidis;Usman A. Khan;Ercan Kuruoglu;Chiman Kwan;Lifeng Lai;Daniel L. Lau;Xiaohui Liang;Joseph C. Liberti;Liang Liu;Durga P. Malladi;C. Mecklenbraeuker;Joseph A. Paradiso;Vishal M. Patel;Yuxin Peng;Boaz Rafaely;Alejandro R. Ribeiro;Yong Man Ro;Saeed Sanei;George A. Saon;Stephan Schlamminger;Farhana Sheikh;Chao Shen;Brian Telfer;Wenwu Wang;Jing Yang;Yi Yang;Kai Yu;Wei Yu;Xiaojun Yuan;Jun Zhou;Asli Celikyilmaz;Haohong Wang","doi":"10.1109/MSP.2026.3652290","DOIUrl":"https://doi.org/10.1109/MSP.2026.3652290","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 6","pages":"4-6"},"PeriodicalIF":9.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11364197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1109/MSP.2026.3652392
Michaël Unser;Dong Yu;Barry D. Van Veen;KVS Hari;John G. Apostolopoulos;Daniel P. W. Ellis;Santiago Segarra;Katherine L. Bouman;Hongkang Li;Nicholas Chimitt;Burak Varici
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
提供社会信息，可能包括新闻，评论或技术笔记，从业者和研究人员应该感兴趣。
{"title":"2025 IEEE Signal Processing Society Awards [Society News]","authors":"Michaël Unser;Dong Yu;Barry D. Van Veen;KVS Hari;John G. Apostolopoulos;Daniel P. W. Ellis;Santiago Segarra;Katherine L. Bouman;Hongkang Li;Nicholas Chimitt;Burak Varici","doi":"10.1109/MSP.2026.3652392","DOIUrl":"https://doi.org/10.1109/MSP.2026.3652392","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 6","pages":"13-15"},"PeriodicalIF":9.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11364180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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