Pub Date : 2025-01-07DOI: 10.1109/MSP.2025.3526404
{"title":"2024 Index IEEE Signal Processing Magazine Vol. 41","authors":"","doi":"10.1109/MSP.2025.3526404","DOIUrl":"10.1109/MSP.2025.3526404","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 6","pages":"105-121"},"PeriodicalIF":9.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10830759","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936041","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 : 2025-01-01DOI: 10.1109/MSP.2024.3495272
Kostas Plataniotis
{"title":"Our Fall Flagship Event: A Story of Past Accomplishments and Proposed Innovations [President’s Message]","authors":"Kostas Plataniotis","doi":"10.1109/MSP.2024.3495272","DOIUrl":"10.1109/MSP.2024.3495272","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 6","pages":"6-7"},"PeriodicalIF":9.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819700","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911842","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 : 2025-01-01DOI: 10.1109/MSP.2024.3497727
Sharon Gannot;Walter Kellermann;Zbyněk Koldovský;Shoko Araki;Gaël Richard
{"title":"Special Issue on Model-Based and Data-Driven Audio Signal Processing [From the Guest Editors]","authors":"Sharon Gannot;Walter Kellermann;Zbyněk Koldovský;Shoko Araki;Gaël Richard","doi":"10.1109/MSP.2024.3497727","DOIUrl":"10.1109/MSP.2024.3497727","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 6","pages":"8-11"},"PeriodicalIF":9.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819694","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911844","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 : 2025-01-01DOI: 10.1109/MSP.2024.3449557
Ernst Seidel;Gerald Enzner;Pejman Mowlaee;Tim Fingscheidt
Kalman filtering is a powerful approach to adaptive filtering for various problems in signal processing. The frequency-domain adaptive Kalman filter (FDKF), based on the concept of the acoustic state space, provides a unifying solution to the adaptive filter update and the related stepsize control. It was conceived for the problem of acoustic echo cancellation and, as such, is frequently applied in hands-free systems. This article motivates and briefly recapitulates the linear FDKF and investigates how it can be further supported by deep neural networks (DNNs) in various ways, specifically to overcome the challenges and limitations related to the usually required estimation of process and observation noise covariances for the Kalman filter. While the mere FDKF comes with very low computational complexity, its neural Kalman filter variants may deliver faster (re)convergence, better echo cancellation, and even exceed the FDKF in its excellent double-talk near-end speech preservation both under linear and nonlinear loudspeaker conditions. To provide a synopsis of the state of the art, this article contributes a comparison of a range of DNN-based extensions of FDKF in the same training framework and using the same data.
{"title":"Neural Kalman Filters for Acoustic Echo Cancellation: Comparison of deep neural network-based extensions [Special Issue On Model-Based and Data-Driven Audio Signal Processing]","authors":"Ernst Seidel;Gerald Enzner;Pejman Mowlaee;Tim Fingscheidt","doi":"10.1109/MSP.2024.3449557","DOIUrl":"10.1109/MSP.2024.3449557","url":null,"abstract":"Kalman filtering is a powerful approach to adaptive filtering for various problems in signal processing. The frequency-domain adaptive Kalman filter (FDKF), based on the concept of the acoustic state space, provides a unifying solution to the adaptive filter update and the related stepsize control. It was conceived for the problem of acoustic echo cancellation and, as such, is frequently applied in hands-free systems. This article motivates and briefly recapitulates the linear FDKF and investigates how it can be further supported by deep neural networks (DNNs) in various ways, specifically to overcome the challenges and limitations related to the usually required estimation of process and observation noise covariances for the Kalman filter. While the mere FDKF comes with very low computational complexity, its neural Kalman filter variants may deliver faster (re)convergence, better echo cancellation, and even exceed the FDKF in its excellent double-talk near-end speech preservation both under linear and nonlinear loudspeaker conditions. To provide a synopsis of the state of the art, this article contributes a comparison of a range of DNN-based extensions of FDKF in the same training framework and using the same data.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 6","pages":"24-38"},"PeriodicalIF":9.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911992","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 : 2025-01-01DOI: 10.1109/MSP.2024.3495292
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":"The IEEE Signal Processing Society (SPS) Announces the 2025 Class of Distinguished Lecturers and Distinguished Industry Speakers [Society News]","authors":"","doi":"10.1109/MSP.2024.3495292","DOIUrl":"10.1109/MSP.2024.3495292","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":"41 6","pages":"100-104"},"PeriodicalIF":9.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911907","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 : 2025-01-01DOI: 10.1109/MSP.2024.3465896
Shoichi Koyama;Juliano G. C. Ribeiro;Tomohiko Nakamura;Natsuki Ueno;Mirco Pezzoli
The area of study concerning the estimation of spatial sound, i.e., the distribution of a physical quantity of sound such as acoustic pressure, is called sound field estimation, which is the basis for various applied technologies related to spatial audio processing. The sound field estimation problem is formulated as a function interpolation problem in machine learning in a simplified scenario. However, high estimation performance cannot be expected by simply applying general interpolation techniques that rely only on data. The physical properties of sound fields are useful a priori information, and it is considered extremely important to incorporate them into the estimation. In this article, we introduce the fundamentals of physics-informed machine learning (PIML) for sound field estimation and overview current PIML-based sound field estimation methods.
{"title":"Physics-Informed Machine Learning for Sound Field Estimation: Fundamentals, state of the art, and challenges [Special Issue On Model-Based and Data-Driven Audio Signal Processing]","authors":"Shoichi Koyama;Juliano G. C. Ribeiro;Tomohiko Nakamura;Natsuki Ueno;Mirco Pezzoli","doi":"10.1109/MSP.2024.3465896","DOIUrl":"10.1109/MSP.2024.3465896","url":null,"abstract":"The area of study concerning the estimation of spatial sound, i.e., the distribution of a physical quantity of sound such as acoustic pressure, is called sound field estimation, which is the basis for various applied technologies related to spatial audio processing. The sound field estimation problem is formulated as a function interpolation problem in machine learning in a simplified scenario. However, high estimation performance cannot be expected by simply applying general interpolation techniques that rely only on data. The physical properties of sound fields are useful a priori information, and it is considered extremely important to incorporate them into the estimation. In this article, we introduce the fundamentals of \u0000<italic>physics-informed machine learning (PIML)</i>\u0000 for sound field estimation and overview current PIML-based sound field estimation methods.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 6","pages":"60-71"},"PeriodicalIF":9.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911909","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}
Distant speech processing is a critical downstream application in speech and audio signal processing. Traditionally, researchers have addressed this challenge by breaking it down into distinct subproblems and encompassing the extraction of clean speech signals from noisy inputs, feature extraction, and transcription. This approach led to the development of modular distant automatic speech recognition (DASR) models, which are often designed with multiple stages in cascade, corresponding to specific subproblems. Recently, the surge in the capabilities of deep learning is propelling the popularity of purely end-to-end (E2E) models that employ a single large neural network to tackle an entire DASR task in an extremely data-driven manner. However, an alternative paradigm persists in the form of a modular model design, where we can often leverage speech and signal processing models. Although this approach mirrors the multistage model, it is trained through an E2E process. This article overviews the recent development of DASR systems, focusing on E2E module-based models and showcasing successful downstream applications of model-based and data-driven audio signal processing.
{"title":"Module-Based End-to-End Distant Speech Processing: A case study of far-field automatic speech recognition [Special Issue On Model-Based and Data-Driven Audio Signal Processing]","authors":"Xuankai Chang;Shinji Watanabe;Marc Delcroix;Tsubasa Ochiai;Wangyou Zhang;Yanmin Qian","doi":"10.1109/MSP.2024.3486469","DOIUrl":"10.1109/MSP.2024.3486469","url":null,"abstract":"Distant speech processing is a critical downstream application in speech and audio signal processing. Traditionally, researchers have addressed this challenge by breaking it down into distinct subproblems and encompassing the extraction of clean speech signals from noisy inputs, feature extraction, and transcription. This approach led to the development of modular distant automatic speech recognition (DASR) models, which are often designed with multiple stages in cascade, corresponding to specific subproblems. Recently, the surge in the capabilities of deep learning is propelling the popularity of purely end-to-end (E2E) models that employ a single large neural network to tackle an entire DASR task in an extremely data-driven manner. However, an alternative paradigm persists in the form of a modular model design, where we can often leverage speech and signal processing models. Although this approach mirrors the multistage model, it is trained through an E2E process. This article overviews the recent development of DASR systems, focusing on E2E module-based models and showcasing successful downstream applications of model-based and data-driven audio signal processing.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 6","pages":"39-50"},"PeriodicalIF":9.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911947","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 : 2025-01-01DOI: 10.1109/MSP.2024.3518134
Tülay Adali
{"title":"An Exciting Juncture: The Convergence of Machine Learning and Signal Processing [From the Editor]","authors":"Tülay Adali","doi":"10.1109/MSP.2024.3518134","DOIUrl":"10.1109/MSP.2024.3518134","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 6","pages":"3-5"},"PeriodicalIF":9.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911908","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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