General Audio Signal Processing with Deep Learning
暫譯: 深度學習的通用音頻信號處理

Xu, Kele, Bai, Jisheng, Zhu, Boqing

General Audio Signal Processing with Deep Learning
  • 出版商: Springer
  • 出版日期: 2026-06-26
  • 售價: $8,420
  • 貴賓價: 9.5 折 $7,999
  • 語言: 英文
  • 頁數: 676
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819508355
  • ISBN-13: 9789819508358
  • 相關分類: DeepLearning

    • 海外代購書籍(需單獨結帳)

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商品描述

Dive into the cutting-edge integration of deep learning with audio signal processing in this authoritative guide. Designed for audio engineers, data scientists, and tech enthusiasts, this book demystifies the complex world of deep neural networks, including CNNs and RNNs, and their applications in speech recognition, music transcription, and sound event detection.

Explore the practical side of deep learning with hands-on tutorials using TensorFlow and PyTorch, building your intuition for model architectures and hyperparameter tuning. Gain insights into real-world deployment challenges, from data preprocessing to model evaluation, interpretability, and scalability. Industry case studies and best practices illuminate the path to building efficient and effective deep learning-based audio systems.

This book empowers you with the knowledge to leverage the full potential of deep learning in audio processing, offering a comprehensive resource for tackling sophisticated audio tasks. Whether you're a researcher, engineer, or enthusiast, this guide is your key to mastering the synergy of audio signal processing and deep learning, ensuring you approach audio-related challenges with confidence and proficiency.

商品描述(中文翻譯)

深入探索深度學習與音訊信號處理的前沿整合，這本權威指南專為音訊工程師、數據科學家和科技愛好者而設計，揭開深度神經網絡（包括 CNN 和 RNN）及其在語音識別、音樂轉譯和聲音事件檢測中的應用的複雜世界。

透過使用 TensorFlow 和 PyTorch 的實作教程，探索深度學習的實務面，培養對模型架構和超參數調整的直覺。深入了解從數據預處理到模型評估、可解釋性和可擴展性等現實部署挑戰。行業案例研究和最佳實踐照亮了建立高效且有效的基於深度學習的音訊系統的道路。

這本書賦予您利用深度學習在音訊處理中充分潛力的知識，提供一個全面的資源來應對複雜的音訊任務。無論您是研究人員、工程師還是愛好者，這本指南都是您掌握音訊信號處理與深度學習協同作用的關鍵，確保您能自信且熟練地應對與音訊相關的挑戰。

作者簡介

Kele Xu (Senior Member, IEEE) is currently an Associate Professor with the School of Computer Science, National University of Defense Technology, Changsha, China. His research interests include audio signal processing, machine learning, and intelligent software systems. He serves as an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology and a Guest Editor for Science Partner Journal Cyborg and Bionic Systems. He has co-authored more than 100 publications in peer-reviewed journals and conference proceedings, including ICLR, NeurIPS, CVPR, ICML, TASLP, TAI, TMI, JASA, AAAI, IJCAI, ASE, ACM MM, and ICASSP.

Jisheng Bai is currently a Lecturer with the Center for Image and Information Processing, School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China. His research interests focus on deep learning and audio processing, with particular emphasis on bridging fundamental algorithms with practical deployment in speech enhancement, acoustic sensing, and intelligent audio systems.

Boqing Zhu is a Research Fellow at the National University of Defense Technology, China, specializing in computational acoustics, underwater acoustic signal processing, and continual learning methodologies. His research lies at the intersection of traditional signal processing and modern machine learning, with a particular focus on the foundations of deep learning, continual learning paradigms, and their applications to underwater acoustic sensing, audio synthesis, and audio-visual learning.

Qisheng Xu is a Ph.D. candidate at the College of Computer Science and Technology, National University of Defense Technology, China. His research interests include audio signal processing, continual learning algorithms, and intelligent computing.

Yi Su is currently pursuing the Ph.D. degree in Computer Science at the National University of Defense Technology, Changsha, China. Her research interests include pattern recognition, data engineering, and multimodal signal processing, with a particular focus on audio-language modeling and cross-modal understanding.

Mou Wang is currently a Postdoctoral Researcher with the Institute of Acoustics, Chinese Academy of Sciences, Beijing, China. His research interests include machine learning and speech signal processing, with a particular focus on speech enhancement and audio denoising techniques. He has received several distinctions, including the Excellent Paper Award at the International Conference on Ubi-Media Computing and Workshops in 2019, the Best Paper Award at the 19th National Conference on Man-Machine Speech Communication in 2024, and the Outstanding Reviewer recognition from IEEE Transactions on Multimedia in 2022.

作者簡介(中文翻譯)

Kele Xu（IEEE 高級會員）目前是中國長沙國防科技大學計算機科學學院的副教授。他的研究興趣包括音頻信號處理、機器學習和智能軟體系統。他擔任《IEEE 影像技術電路與系統期刊》的副編輯，以及《科學合作夥伴期刊：機器人與仿生系統》的客座編輯。他共同撰寫了超過100篇在同行評審的期刊和會議論文中的出版物，包括ICLR、NeurIPS、CVPR、ICML、TASLP、TAI、TMI、JASA、AAAI、IJCAI、ASE、ACM MM和ICASSP。

Jisheng Bai目前是中國西安郵電大學通訊與信息工程學院圖像與信息處理中心的講師。他的研究興趣集中在深度學習和音頻處理，特別強調將基本算法與語音增強、聲學感知和智能音頻系統的實際部署相結合。

Boqing Zhu是中國國防科技大學的研究員，專注於計算聲學、水下聲音信號處理和持續學習方法。他的研究位於傳統信號處理和現代機器學習的交叉點，特別關注深度學習的基礎、持續學習範式及其在水下聲學感知、音頻合成和視聽學習中的應用。

Qisheng Xu是中國國防科技大學計算機科學與技術學院的博士候選人。他的研究興趣包括音頻信號處理、持續學習算法和智能計算。

Yi Su目前在中國長沙國防科技大學攻讀計算機科學的博士學位。她的研究興趣包括模式識別、數據工程和多模態信號處理，特別專注於音頻-語言建模和跨模態理解。

Mou Wang目前是中國北京中國科學院聲學研究所的博士後研究員。他的研究興趣包括機器學習和語音信號處理，特別專注於語音增強和音頻去噪技術。他獲得了多項榮譽，包括2019年在國際普及媒體計算與研討會上獲得的優秀論文獎、2024年第19屆全國人機語音通信會議的最佳論文獎，以及2022年獲得的IEEE 多媒體期刊的傑出審稿人認可。

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