IEEE Signal Processing Magazine最新文献

2024 Index IEEE Signal Processing Magazine Vol. 41 2024索引IEEE信号处理杂志第41卷

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-07 DOI: 10.1109/MSP.2025.3526404

引用次数: 0

Our Fall Flagship Event: A Story of Past Accomplishments and Proposed Innovations [President’s Message] 我们的秋季旗舰活动：过去的成就和拟议的创新的故事[总统讲话]

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-01 DOI: 10.1109/MSP.2024.3495272

Kostas Plataniotis

引用次数: 0

Special Issue on Model-Based and Data-Driven Audio Signal Processing [From the Guest Editors] 基于模型和数据驱动的音频信号处理特刊[来自特邀编辑]

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-01 DOI: 10.1109/MSP.2024.3497727

Sharon Gannot;Walter Kellermann;Zbyněk Koldovský;Shoko Araki;Gaël Richard

引用次数: 0

IEEE Feedback IEEE反馈

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-01 DOI: 10.1109/MSP.2024.3512693

引用次数: 0

SPS Resource Center SPS资源中心

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-01 DOI: 10.1109/MSP.2024.3512692

引用次数: 0

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] 声学回声消除的神经卡尔曼滤波器：基于深度神经网络扩展的比较[基于模型和数据驱动的音频信号处理特刊]

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-01 DOI: 10.1109/MSP.2024.3449557

Ernst Seidel;Gerald Enzner;Pejman Mowlaee;Tim Fingscheidt

{"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}

引用次数: 0

The IEEE Signal Processing Society (SPS) Announces the 2025 Class of Distinguished Lecturers and Distinguished Industry Speakers [Society News] IEEE信号处理学会（SPS）公布2025年杰出讲师和杰出行业演讲者名单[社会新闻]

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-01 DOI: 10.1109/MSP.2024.3495292

引用次数: 0

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] 基于物理的声场估计机器学习：基础、现状和挑战[基于模型和数据驱动的音频信号处理特刊]

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-01 DOI: 10.1109/MSP.2024.3465896

Shoichi Koyama;Juliano G. C. Ribeiro;Tomohiko Nakamura;Natsuki Ueno;Mirco Pezzoli

引用次数: 0

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] 基于模块的端到端远程语音处理：远场自动语音识别的案例研究[基于模型和数据驱动的音频信号处理专题]

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-01 DOI: 10.1109/MSP.2024.3486469

Xuankai Chang;Shinji Watanabe;Marc Delcroix;Tsubasa Ochiai;Wangyou Zhang;Yanmin Qian

{"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}

引用次数: 0

An Exciting Juncture: The Convergence of Machine Learning and Signal Processing [From the Editor] 激动人心的时刻：机器学习和信号处理的融合

IF 9.4 1区工程技术

IEEE Signal Processing Magazine Pub Date : 2025-01-01 DOI: 10.1109/MSP.2024.3518134

Tülay Adali

引用次数: 0