{"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":null,"pages":null},"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}
{"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":null,"pages":null},"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}
Vincent W. Neo;Soydan Redif;John G. McWhirter;Jennifer Pestana;Ian K. Proudler;Stephan Weiss;Patrick A. Naylor
{"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":null,"pages":null},"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}
{"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":null,"pages":null},"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}
Sharon Gannot;Zheng-Hua Tan;Martin Haardt;Nancy F. Chen;Hoi-To Wai;Ivan Tashev;Walter Kellermann;Justin Dauwels
{"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":null,"pages":null},"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}
{"title":"Call for Papers - IEEE Signal Processing Magazine","authors":"","doi":"10.1109/MSP.2023.3313848","DOIUrl":"https://doi.org/10.1109/MSP.2023.3313848","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10313220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903517","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}
{"title":"SPS Members, You Are All Heirs of Fourier! [From the Editor]","authors":"Christian Jutten","doi":"10.1109/MSP.2023.3318848","DOIUrl":"https://doi.org/10.1109/MSP.2023.3318848","url":null,"abstract":"My three years of service as the editor-in-chief (EIC) of \u0000<italic>Signal Processing Magazine</i>\u0000 (\u0000<italic>SPM</i>\u0000) are now coming to a close. During the past three years, many of us were deeply affected by serious political, social, and environmental events such as the war in Ukraine; protests for freedom in Iran; coups d’état in Africa; the COVID-19 pandemic; seisms in Turkey, Syria, and Morocco; huge floods in Libya and India; gigantic fires in North America and Southern Europe; and an avalanche of stones in the Alps, to name a few. In such a context, I believe that the IEEE slogan, “Advancing Technology for Humanity,” is incredibly relevant and timely. It also must be viewed in a wider sense, including the preservation of Earth and sustainable development. In point of fact, what would become of humanity without Earth? I believe that we must always have this in mind when contemplating our future projects, asking for funding, and while teaching.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10313212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903521","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}
{"title":"Synthetic Speech Attribution: Highlights From the IEEE Signal Processing Cup 2022 Student Competition [SP Competitions]","authors":"Davide Salvi;Clara Borrelli;Paolo Bestagini;Fabio Antonacci;Matthew Stamm;Lucio Marcenaro;Angshul Majumdar","doi":"10.1109/MSP.2023.3268823","DOIUrl":"10.1109/MSP.2023.3268823","url":null,"abstract":"The possibility of manipulating digital multimedia material is nowadays within everyone’s reach. In the audio case, anybody can create fake synthetic speech tracks using various methods with almost no effort \u0000<xref>[1]</xref>\u0000. These methods range from simple waveform concatenation operations to more complex neural networks \u0000<xref>[2]</xref>\u0000, \u0000<xref>[3]</xref>\u0000.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":null,"pages":null},"PeriodicalIF":14.9,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45490653","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}