Deep Learning for Multimedia Processing Applications: Volume Two: Signal Processing and Pattern Recognition

Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.

Divided into two volumes, Volume Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.

Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.

Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.

《多媒體處理的深度學習應用》是一本全面探討深度學習技術在多媒體處理領域革命性影響的指南。本書針對從學生到專業人士的廣泛讀者，提供了深度學習在各種多媒體領域中的應用的簡明易懂的概述，包括圖像處理、視頻分析、音頻識別和自然語言處理。

本書分為兩卷，第二卷深入介紹了卷積神經網絡（CNN）、循環神經網絡（RNN）和生成對抗網絡（GAN）等高級主題，解釋了它們在多媒體任務中的獨特能力。讀者將了解到深度學習技術如何實現準確高效的圖像識別、物體檢測、語義分割和圖像合成。本書還介紹了視頻分析技術，包括動作識別、視頻字幕生成和視頻生成，突出了深度學習在從視頻中提取有意義信息方面的作用。

此外，本書還探討了使用深度學習模型進行語音識別、音樂分類和聲音事件檢測等音頻處理任務。它演示了深度學習算法如何有效處理音頻數據，為多媒體應用開拓了新的可能性。最後，本書還探討了深度學習與自然語言處理技術的整合，使系統能夠理解、生成和解釋多媒體上下文中的文本信息。

在整本書中，提供了實用的例子、代碼片段和真實案例研究，幫助讀者實踐深度學習在多媒體處理中的解決方案。《多媒體處理的深度學習應用》是任何有興趣利用深度學習發掘多媒體數據巨大潛力的人的必備資源。

Uzair Aslam Bhatti was born in 1986. He received a PhD degree in information and communication engineering from Hainan University, Haikou, Hainan, in 2019. He completed his postdoctoral from Nanjing Normal University, Nanjing, China, in implementing Clifford algebra algorithms in analyzing the geospatial data using artificial intelligence (AI). He is currently working as an associate professor in the School of Information and Communication Engineering at Hainan University. His areas of specialty include AI, machine learning, and image processing. He is serving as a guest editor of various journals including Frontier in Plant Science, Frontier in Environmental Science, Computer Materials and Continua, Plos One, IEEE Access, etc., and has reviewed many IEEE Transactions and Elsevier journals.

Jingbing Li is a doctor, professor, doctoral supervisor, and the vice president of the Hainan Provincial Invention Association. He has been awarded honorary titles of Leading Talents in Hainan Province, Famous Teaching Teachers in Hainan Province, Outstanding Young and Middle-aged Backbone Teachers in Hainan Province, and Excellent Teachers in Baosteel. He has also won the second prize of the Hainan Provincial Science and Technology Progress Award three times (the first completer twice, the second completer once). He has obtained 13 authorized national invention patents, published 5 monographs, such as medical image digital watermarking, and published more than 80 SCI/EI retrieved academic papers (including 22 SCI retrieved papers) as the first author or corresponding author. He has presided over two projects of the National Natural Science Foundation of China and five projects of Hainan Province's key research and development projects and Hainan Province's international scientific and technological cooperation projects.

Dr. Mengxing Huang is the dean of the School of Information at Hainan University. He has occupied many roles, such as the leader of the talent team of "Smart Service", the chief scientist of the National Key R&D Program, a member of the Expert Committee of Artificial Intelligence and Blockchain of the Science and Technology Committee of the Ministry of Education, the executive director of the Postgraduate Education Branch of the China Electronics Education Society, and the Computer Professional Teaching Committee of the Ministry of Education, among others. His main research areas include big data and intelligent information processing, multi-source information perception and fusion, artificial intelligence and intelligent services, etc. In recent years, he has published more than 230 academic papers as the first author and corresponding author, obtained 36 invention patents authorized by the state and 96 software copyrights, published 4 monographs, and translated 2 books. He won first prize and second prize of the Hainan Provincial Science and Technology Progress Award as the first person who completed it, and he won two Hainan Provincial Excellent Teaching Achievement Awards and the Excellent Teacher Award. He has presided over and undertaken more than 30 national, provincial, and ministerial-level projects, such as national key research and development plan projects, national science and technology support plans, and National Natural Science Foundation projects.

Sibghat Ullah Bazai completed his undergraduate and graduate studies in computer engineering at the Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS) in Quetta, Pakistan. He received his PhD (IT) in cybersecurity from Massey University in Auckland, New Zealand, in 2020. As part of his research, he is interested in applying cybersecurity, identifying diseases with deep learning, automating exams with natural language processing, developing local language sentiment data sets, and planning smart cities. Sibghat is a guest editor and reviewer for several journals' special issues in MDPI, Hindawi, CMC, PlosOne, Frontier, and others.

Muhammad Aamir received a bachelor of engineering degree in computer systems engineering from Mehran University of Engineering & Technology Jamshoro, Sindh, Pakistan, in 2008; a master of engineering degree in software engineering from Chongqing University, China, in 2014; and a PhD degree in computer science and technology from Sichuan University, Chengdu, China, in 2019. He is currently an associate professor at the Department of Computer, Huanggang Normal University, China. His main research interests include pattern recognition, computer vision, image processing, deep learning, and fractional calculus.

Uzair Aslam Bhatti於1986年出生。他於2019年在海南大學獲得信息與通信工程博士學位。他在中國南京師範大學完成了博士後研究，並利用人工智能（AI）分析地理空間數據時實施了克利福德代數算法。他目前在海南大學信息與通信工程學院擔任副教授。他的專業領域包括人工智能、機器學習和圖像處理。他擔任多個期刊的客座編輯，包括《Frontier in Plant Science》、《Frontier in Environmental Science》、《Computer Materials and Continua》、《Plos One》、《IEEE Access》等，並審查了許多IEEE Transactions和Elsevier期刊。

Jingbing Li是一位博士、教授、博士生導師，也是海南省發明協會副會長。他獲得了海南省領軍人才、海南省著名教學名師、海南省優秀中青年骨幹教師和寶鋼優秀教師等榮譽稱號。他三次獲得海南省科技進步獎二等獎（兩次為第一完成人，一次為第二完成人）。他獲得了13項國家發明專利授權，出版了5本專著，如醫學圖像數字水印等，並以第一作者或通訊作者身份發表了80多篇SCI/EI檢索的學術論文（其中包括22篇SCI檢索論文）。他主持了中國國家自然科學基金項目兩項，海南省重點研發項目和海南省國際科技合作項目各五項。

Dr. Mengxing Huang是海南大學信息學院院長。他擔任了許多職務，如“智慧服務”人才團隊的領導者，國家重點研發計劃的首席科學家，教育部人工智能和區塊鏈專家委員會的專家委員，中國電子教育學會研究生教育分會執行理事，以及教育部計算機專業教學委員會等。他的主要研究領域包括大數據和智能信息處理、多源信息感知與融合、人工智能和智能服務等。近年來，他以第一作者和通訊作者身份發表了230多篇學術論文，獲得了國家授權的36項發明專利和96項軟件著作權，出版了4本專著，並翻譯了2本書。他作為第一完成人兩次獲得了海南省科技進步獎一等獎和二等獎，並獲得了兩項海南省優秀教學成果獎和優秀教師獎。他主持和承擔了30多項國家、省部級項目，包括國家重點研發計劃項目、國家科技支撐計劃和國家自然科學基金項目。

Sibghat Ullah Bazai在巴基斯坦奎達的巴洛奇斯坦信息技術、工程和管理科學大學（BUITEMS）完成了本科和研究生學業。他於2020年在紐西蘭奧克蘭的梅西大學獲得了信息技術領域的博士學位。作為他的研究的一部分，他對應用網絡安全、深度學習識別疾病、自然語言處理自動化考試、開發本地語言情感數據集和規劃智慧城市感興趣。Sibghat是一位客座編輯和審稿人。