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，可能包括从业者和研究人员感兴趣的新闻、评论或技术笔记。
{"title":"Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition [SP Competitions]","authors":"Davide Cozzolino;Koki Nagano;Lucas Thomaz;Angshul Majumdar;Luisa Verdoliva","doi":"10.1109/MSP.2023.3294720","DOIUrl":"https://doi.org/10.1109/MSP.2023.3294720","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":"40 7","pages":"94-100"},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10313227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71902877","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 : 2023-11-08DOI: 10.1109/MSP.2023.3311592
Ruiming Guo;Thierry Blu
This article introduces a simple formula that provides the exact frequency of a pure sinusoid from just two samples of its discrete-time Fourier transform (DTFT). Even when the signal is not a pure sinusoid, this formula still works in a very good approximation (optimally after a single refinement), paving the way for the high-resolution frequency tracking of quickly varying signals or simply improving the frequency resolution of the peaks of a discrete Fourier transform (DFT).
{"title":"Super-Resolving a Frequency Band [Tips & Tricks]","authors":"Ruiming Guo;Thierry Blu","doi":"10.1109/MSP.2023.3311592","DOIUrl":"https://doi.org/10.1109/MSP.2023.3311592","url":null,"abstract":"This article introduces a simple formula that provides the exact frequency of a pure sinusoid from just two samples of its discrete-time Fourier transform (DTFT). Even when the signal is not a pure sinusoid, this formula still works in a very good approximation (optimally after a single refinement), paving the way for the high-resolution frequency tracking of quickly varying signals or simply improving the frequency resolution of the peaks of a discrete Fourier transform (DFT).","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"40 7","pages":"73-77"},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71902926","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 : 2023-11-08DOI: 10.1109/MSP.2023.3322050
Athina Petropulu
My end of term as IEEE Signal Processing Society (SPS) president is fast approaching. It has been an incredible experience that has provided me with so many opportunities to engage with our members around the globe, forge relationships with other IEEE Societies, and meet a diverse range of people that I hope will become active members of our Society in the future. It has been a great privilege to be at the helm of a Society that garners such a high level of worldwide respect and recognition. It has also provided me with the chance to learn, identify the challenges we still face, anticipate future challenges, and work to find solutions that will make our Society, and the world, a better place.
{"title":"Reflections on the Poland Chapter Celebration [President’s Message]","authors":"Athina Petropulu","doi":"10.1109/MSP.2023.3322050","DOIUrl":"https://doi.org/10.1109/MSP.2023.3322050","url":null,"abstract":"My end of term as IEEE Signal Processing Society (SPS) president is fast approaching. It has been an incredible experience that has provided me with so many opportunities to engage with our members around the globe, forge relationships with other IEEE Societies, and meet a diverse range of people that I hope will become active members of our Society in the future. It has been a great privilege to be at the helm of a Society that garners such a high level of worldwide respect and recognition. It has also provided me with the chance to learn, identify the challenges we still face, anticipate future challenges, and work to find solutions that will make our Society, and the world, a better place.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"40 7","pages":"7-10"},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10313213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903522","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 : 2023-11-08DOI: 10.1109/MSP.2023.3294721
Shlomo Engelberg
Moving average filters output the average of N samples, and it is easy to see (and to prove) that they are low-pass filters.
移动平均滤波器输出N个样本的平均值，很容易看出（并证明）它们是低通滤波器。
{"title":"Implementing Moving Average Filters Using Recursion [Tips & Tricks]","authors":"Shlomo Engelberg","doi":"10.1109/MSP.2023.3294721","DOIUrl":"https://doi.org/10.1109/MSP.2023.3294721","url":null,"abstract":"Moving average filters output the average of \u0000<italic>N</i>\u0000 samples, and it is easy to see (and to prove) that they are low-pass filters.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"40 7","pages":"78-80"},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71902874","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 : 2023-11-08DOI: 10.1109/MSP.2023.3290772
David Shiung;Jeng-Ji Huang;Ya-Yin Yang
Designing filters with perfect frequency responses (i.e., flat passbands, sharp transition bands, highly suppressed stopbands, and linear phase responses) is always the ultimate goal of any digital signal processing (DSP) practitioner. High-order finite impulse response (FIR) filters may meet these requirements when we put no constraint on implementation complexity. In contrast to FIR filters, infinite impulse response (IIR) filters, owing to their recursive structures, provide an efficient way for high-performance filtering at reduced complexity. However, also due to their recursive structure, IIR filters inherently have nonlinear phase responses, and this does restrain their applicability. In this article, we propose two tricks regarding cascading a prototype IIR filter with a few shaping all-pass filters (APFs) for an almost linear phase response over its passband. After performing a delicate design on the prototype and shaping filters, we approach perfect filtering with reduced complexity.
{"title":"Tricks for Designing a Cascade of Infinite Impulse Response Filters With an Almost Linear Phase Response [Tips & Tricks]","authors":"David Shiung;Jeng-Ji Huang;Ya-Yin Yang","doi":"10.1109/MSP.2023.3290772","DOIUrl":"https://doi.org/10.1109/MSP.2023.3290772","url":null,"abstract":"Designing filters with perfect frequency responses (i.e., flat passbands, sharp transition bands, highly suppressed stopbands, and linear phase responses) is always the ultimate goal of any digital signal processing (DSP) practitioner. High-order finite impulse response (FIR) filters may meet these requirements when we put no constraint on implementation complexity. In contrast to FIR filters, infinite impulse response (IIR) filters, owing to their recursive structures, provide an efficient way for high-performance filtering at reduced complexity. However, also due to their recursive structure, IIR filters inherently have nonlinear phase responses, and this does restrain their applicability. In this article, we propose two tricks regarding cascading a prototype IIR filter with a few shaping all-pass filters (APFs) for an almost linear phase response over its passband. After performing a delicate design on the prototype and shaping filters, we approach perfect filtering with reduced complexity.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"40 7","pages":"64-73"},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71902925","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 : 2023-11-08DOI: 10.1109/MSP.2023.3269200
Vincent W. Neo;Soydan Redif;John G. McWhirter;Jennifer Pestana;Ian K. Proudler;Stephan Weiss;Patrick A. Naylor
This article is devoted to the polynomial eigenvalue decomposition (PEVD) and its applications in broadband multichannel signal processing, motivated by the optimum solutions provided by the EVD for the narrowband case [1] , [2] . In general, we would like to extend the utility of the EVD to also address broadband problems. Multichannel broadband signals arise at the core of many essential commercial applications, such as telecommunications, speech processing, health-care monitoring, astronomy and seismic surveillance, and military technologies, including radar, sonar, and communications [3] . The success of these applications often depends on the performance of signal processing tasks, including data compression [4] , source localization [5] , channel coding [6] , signal enhancement [7] , beamforming [8] , and source separation [9] . In most cases and for narrowband signals, performing an EVD is the key to the signal processing algorithm. Therefore, this article aims to introduce the PEVD as a novel mathematical technique suitable for many broadband signal processing applications.
{"title":"Polynomial Eigenvalue Decomposition for Multichannel Broadband Signal Processing: A mathematical technique offering new insights and solutions","authors":"Vincent W. Neo;Soydan Redif;John G. McWhirter;Jennifer Pestana;Ian K. Proudler;Stephan Weiss;Patrick A. Naylor","doi":"10.1109/MSP.2023.3269200","DOIUrl":"https://doi.org/10.1109/MSP.2023.3269200","url":null,"abstract":"This article is devoted to the polynomial eigenvalue decomposition (PEVD) and its applications in broadband multichannel signal processing, motivated by the optimum solutions provided by the EVD for the narrowband case \u0000<xref>[1]</xref>\u0000, \u0000<xref>[2]</xref>\u0000. In general, we would like to extend the utility of the EVD to also address broadband problems. Multichannel broadband signals arise at the core of many essential commercial applications, such as telecommunications, speech processing, health-care monitoring, astronomy and seismic surveillance, and military technologies, including radar, sonar, and communications \u0000<xref>[3]</xref>\u0000. The success of these applications often depends on the performance of signal processing tasks, including data compression \u0000<xref>[4]</xref>\u0000, source localization \u0000<xref>[5]</xref>\u0000, channel coding \u0000<xref>[6]</xref>\u0000, signal enhancement \u0000<xref>[7]</xref>\u0000, beamforming \u0000<xref>[8]</xref>\u0000, and source separation \u0000<xref>[9]</xref>\u0000. In most cases and for narrowband signals, performing an EVD is the key to the signal processing algorithm. Therefore, this article aims to introduce the PEVD as a novel mathematical technique suitable for many broadband signal processing applications.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"40 7","pages":"18-37"},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903525","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 : 2023-11-08DOI: 10.1109/MSP.2023.3297313
Patrick Flandrin
Joseph Fourier’s methods (and their variants) are omnipresent in audio signal processing. However, it turns out that the underlying ideas took some time to penetrate the field of sound analysis and that different paths were first followed in the period immediately following Fourier’s pioneering work, with or without reference to him. This illustrates the interplay between mathematics and physics as well as the key role played by instrumentation, with notable inventions by outsiders to academia, such as Rudolph Koenig and Édouard-Léon Scott de Martinville.
Joseph傅立叶的方法（及其变体）在音频信号处理中无处不在。然而，事实证明，基本思想需要一段时间才能渗透到声音分析领域，在傅立叶开创性工作之后的这段时间里，无论是否参考了他，都首次走上了不同的道路。这说明了数学和物理学之间的相互作用，以及仪器仪表所发挥的关键作用，学术界的局外人，如Rudolph Koenig和Édouard-Léon Scott de Martinville的著名发明。
{"title":"Fourier and the Early Days of Sound Analysis [DSP History]","authors":"Patrick Flandrin","doi":"10.1109/MSP.2023.3297313","DOIUrl":"https://doi.org/10.1109/MSP.2023.3297313","url":null,"abstract":"Joseph Fourier’s methods (and their variants) are omnipresent in audio signal processing. However, it turns out that the underlying ideas took some time to penetrate the field of sound analysis and that different paths were first followed in the period immediately following Fourier’s pioneering work, with or without reference to him. This illustrates the interplay between mathematics and physics as well as the key role played by instrumentation, with notable inventions by outsiders to academia, such as Rudolph Koenig and Édouard-Léon Scott de Martinville.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"40 7","pages":"11-88"},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903523","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 : 2023-11-08DOI: 10.1109/MSP.2023.3294709
Sharon Gannot;Zheng-Hua Tan;Martin Haardt;Nancy F. Chen;Hoi-To Wai;Ivan Tashev;Walter Kellermann;Justin Dauwels
In the last decade, the signal processing (SP) community has witnessed a paradigm shift from model-based to data-driven methods. Machine learning (ML)—more specifically, deep learning—methodologies are nowadays widely used in all SP fields, e.g., audio, speech, image, video, multimedia, and multimodal/multisensor processing, to name a few. Many data-driven methods also incorporate domain knowledge to improve problem modeling, especially when computational burden, training data scarceness, and memory size are important constraints.
{"title":"Data Science Education: The Signal Processing Perspective [SP Education]","authors":"Sharon Gannot;Zheng-Hua Tan;Martin Haardt;Nancy F. Chen;Hoi-To Wai;Ivan Tashev;Walter Kellermann;Justin Dauwels","doi":"10.1109/MSP.2023.3294709","DOIUrl":"https://doi.org/10.1109/MSP.2023.3294709","url":null,"abstract":"In the last decade, the signal processing (SP) community has witnessed a paradigm shift from model-based to data-driven methods. Machine learning (ML)—more specifically, deep learning—methodologies are nowadays widely used in all SP fields, e.g., audio, speech, image, video, multimedia, and multimodal/multisensor processing, to name a few. Many data-driven methods also incorporate domain knowledge to improve problem modeling, especially when computational burden, training data scarceness, and memory size are important constraints.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"40 7","pages":"89-93"},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71902876","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}
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