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钱昆

钱昆

教授

学科:计算机科学与技术、生物医学工程

方向:计算机听觉、人工智能医学、智慧医学

电子邮件:qian@bit.edu.cn

办公地址:北京市海淀区中关村南大街5号北京理工大学7号教学楼405

个人简介

钱昆,北京理工大学教授、博导,2021年入选“北理工特立青年学者”支持计划,博士毕业于德国慕尼黑工业大学,师从情感计算与计算机听觉领域顶级专家Bjoern W. Schuller(博雅恩)教授2019年至2021年于日本东京大学从事人工智能医学相关领域研究工作,期间入选全球著名青年科学家基金——“日本学术振兴会外国人特别研究员”项目(成功率:10.6%累计发表高水平学术论文70余篇,其中第一作者/通讯作者发表SCI收录论文18篇,包括IEEE Signal Processing Magazine、IEEE IoTJ、IEEE T-ITS、IEEE J-BHI、IEEE T-ASE、IEEE T-BME、ABME、JASA等领域内国际顶级期刊钱博士现为IEEE高级会员IEEE Transactions on Affective ComputingJCR Q1IF-2020: 10.506、Frontiers in Digital Health、BIO Integration等期刊编委,法国巴黎丝路商学院客座教授,中国留德学者计算机学会人工智能与大数据专家委员会专家委员,长期担任数十种领域内顶尖/权威期刊与国际会议审稿人,与世界顶尖高校如英国帝国理工学院、德国慕尼黑工业大学、日本东京大学等保持深入良好的合作关系,可以推荐团队优秀学者/学生进行学术访问与联合培养,欢迎有志向、爱探索、肯专研的同学主动联系。


教育/培训经历

2014年10月至2018年11月,德国慕尼黑工业大学,工学博士,电气工程与信息技术专业

2011年09月至2014年04月,南京理工大学,工学硕士,信号与信息处理专业

2007年09月至201106月,四川师范大学,工学学士,电子信息工程专业


工作经历

2021年08月至今,北京理工大学,教授、博导,脑健康工程责任教授

2019年09月至2021年07月,日本东京大学,日本学术振兴会外国人特别研究员

2018年11月至201909月,日本东京大学,特任研究员

2018年01月至2018年03月,美国卡内基梅隆大学,访问学者

2016年04月至2016年11月,日本东京工业大学,访问学者

2013年11月至2014年03月,新加坡南洋理工大学,访问学者


学术任职

202108月至今,北京理工大学医工融合研究院脑健康工程责任教授

2021年02月至今,IEEE Senior Member(IEEE高级会员)

2021年01月至今,IEEE Transactions on Affective Computing期刊 Associate Editor

2021年01月至今,BIO Integration期刊 Associate Editor

2021年01月至今,中国留德学者计算机学会 人工智能与大数据专家委员会 专家委员

2020年08月至今,Icelandic Research Fund(冰岛科研基金)函评专家


研究领域

计算机听觉、情感计算、人工智能医学、智能信号与信息处理、脑科学


所获奖励/荣誉

2021年03月,Frontiers in Digital Health期刊“期刊大使奖”

202011月,之江国际青年人才基金优秀成果奖”(智能感知组排名第一

2019年12月,日本学术振兴会外国人特别研究员


讲授课程

《计算机听觉医学应用》(春季学期),32学时


教材及专著

  1. Xi Shao, Kun Qian, Li Zhou, Xin Wang, and Ziping Zhao (Editors), Proceedings of the 8th Conference on Sound and Music Technology, Springer LNEE (vol. 761), Singapore, 210 pages, 2021.

  2. Kun Qian, Liang Zhang, Kezhi Li, and Juan Liu, Machine Learning for Non/Less-Invasive Methods in Health Informatics, Frontiers, in press, 2021.


主持/参与项目

  1. 北京理工大学“特立青年学者支持计划”,100.0万元人民币,主持,2021年08月至2027年08月。

  2. 日本学术振兴会外国人特别研究员基金(含配套科研费),约66.8万元人民币,主持, “Deep Learning for Intensive Longitudinal Biomedical Signals and its Health Related AI Applications”,2019年9月至2021年7月,全球录用率:10.6%。

  3. 之江实验室之江国际青年人才基金,6.0万元人民币,主持:“Heart sound Analysis and its Non-invasive healthcare Applications via Machine Intelligence (HANAMI)”2019年8月至2020年8月,全球录用率:<15.0%。

  1. 德国慕尼黑工业大学--美国卡内基梅隆大学联合博士生科研基金,约2.4万元人民币,主持,“Deep Analysis for General Audio Signal Classification”,2018年1月至2018年3月。

  2. 德国慕尼黑工业大学--日本东京工业大学联合博士生科研基金,约2.0万元人民币,主持,“Fast Recognition of Bird Sounds Data by High Performance Computing System”,2016年4月至2016年11月,全校3个名额。

  3. 南京理工大学硕士生出国/境合作科研基金,6.0万元人民币,主持:“基于声学信号处理与机器学习的鼾声识别系统的研究”,2013年11月至2014年3月,全校10个名额。

  4. 日本文部科学省科研费,参与,“Development and Clinical Application of Personalised IoT System to Control the Risk of Mental and Physical Disorders of Workers”,2020年4月至2023年3月。

  5. 日本文部科学省科研费,参与,“Development of Just-in-Time Adaptive Intervention for Behavioural Modification based on Continuous Psycho-Behavioural Monitoring Under Daily Life”,2017年4月至2020年3月。

  1. 欧盟第七框架重点项目,参与,“Intelligent Systems' Holistic Evolving Analysis of Real-life Universal Speaker Characteristics (iHEARu)”,2014年1月至2018年12月。

  2. 欧盟地平线2020项目,参与,“Multi-Modal Human-Robot Interaction for Teaching and Expanding Social Imagination in Autistic Children (DE-ENIGMA)”,2016年2月至2019年7月。

  3. 欧盟地平线2020项目,参与,“Automatic Sentiment Estimation in the Wild (SEWA)”,2015年2月至2018年7月。


SCI Journal paper

Selected Publications

*: Corresponding Author, #: Co-First Author.

[1] Kun Qian*, Zixing Zhang, Yoshiharu Yamamoto, and Björn W. Schuller, “Artificial Intelligence Internet of Things for the Elderly: From Assisted Living to Health-Care Monitoring”, IEEE Signal Processing Magazine (IF-2020: 12.551), vol. 38, no. 4, pp. 78-88, 2021.

[2] Kun Qian*#, Maximilian Schmitt#, Huaiyuan Zheng*#, Tomoya Koike#, Jing Han, Juan Liu*, Wei Ji*, Junjun Duan, Meishu Song, Zijiang Yang, Zhao Ren, Shuo Liu, Zixing Zhang, Yoshiharu Yamamoto, and Björn W. Schuller, “Computer Audition for Fighting the SARS-CoV-2 Corona Crisis — Introducing the Multi-task Speech Corpus for COVID-19”, IEEE Internet of Things Journal (IF-2020: 9.471), accepted, in press, pp. 1-12, 2021.

[3] Kun Qian*, Tomoya Koike, Kazuhiro Yoshiuchi, Björn W. Schuller, and Yoshiharu Yamamoto, “Can Appliances Understand the Behaviour of Elderly via Machine Learning? A Feasibility Study”, IEEE Internet of Things Journal (IF-2020: 9.471), vol. 8, no. 10, pp. 8343-8355, 2021.

[4] Kun Qian*#, Tomoya Koike#, Toru Nakamura, Björn W. Schuller, and Yoshiharu Yamamoto, “Learning Multimodal Representations for Drowsiness Detection”, IEEE Transactions on Intelligent Transportation Systems (IF-2020: 6.492), accepted, in press, pp. 1-10, 2021.

[5] Kun Qian*, Christoph Janott, Maximilian Schmitt, Zixing Zhang, Clemens Heiser, Werner Hemmert, Yoshiharu Yamamoto, and Björn W. Schuller, “Can Machine Learning Assist Locating the Excitation of Snore Sound? A Review”, IEEE Journal of Biomedical and Health Informatics (IF-2020: 5.772), vol. 25, no. 4, pp. 1233-1246, 2021.

[6] Kun Qian*, Christoph Janott, Vedhas Pandit, Zixing Zhang, Clemens Heiser, Winfried Hohenhorst, Michael Herzog, Werner Hemmert and Björn W. Schuller, “Classification of the Excitation Location of Snore Sounds in the Upper Airway by Acoustic Multi-Feature Analysis”, IEEE Transactions on Biomedical Engineering (IF-2020: 4.538), vol. 64, no. 8, pp. 1731-1741, 2017.

[7] Fengquan Dong#, Kun Qian*#, Zhao Ren*, Alice Baird, Xinjian Li, Zhenyu Dai, Bo Dong, Florian Metze, Yoshiharu Yamamoto, and Björn W. Schuller, “Machine Listening for Heart Status Monitoring: Introducing and Benchmarking HSS–the Heart Sounds Shenzhen Corpus”, IEEE Journal of Biomedical and Health Informatics (IF-2020: 5.772), vol. 24, no. 7, pp. 2082-2092, 2020.

[8] Zengjie Zhang, Kun Qian*, Björn W. Schuller, and Dirk Wollherr, “An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory”, IEEE Transactions on Automation Science and Engineering (IF-2020: 5.083), vol. 18, no. 3, pp. 1144-1156, 2021.

[9] Kun Qian*, Maximilian Schmitt, Christoph Janott, Zixing Zhang, Clemens Heiser, Winfried Hohenhorst, Michael Herzog, Werner Hemmert, and Björn W. Schuller, “A Bag of Wavelet Features for Snore Sound Classification”, Annals of Biomedical Engineering (IF-2020: 3.934), vol. 47, no. 4, pp. 1000-1011, 2019.

[10] Kun Qian*, Zixing Zhang, Alice Baird, and Björn W. Schuller, “Active Learning for Bird Sound Classification via a Kernel-based Extreme Learning Machine”, Journal of the Acoustical Society of America (IF-2020: 1.840), vol. 142, no. 4, pp. 1796-1804, 2017.

EI Journal paper

Selected Publications

*: Corresponding Author, #: Co-First Author.

[1] Jing Han, Kun Qian*, Meishu Song, Zijiang Yang, Zhao Ren, Shuo Liu, Juan Liu*, Huaiyuan Zheng*, Wei Ji*, Tomoya Koike, Xiao Li, Zixing Zhang, Yoshiharu Yamamoto, and Björn W. Schuller, “An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety”, in Proceedings of INTERSPEECH, pp. 4946-4950, Shanghai, China, October 2020.

[2] Tomoya Koike, Kun Qian*, Björn W. Schuller, and Yoshiharu Yamamoto, “Learning Higher Representations from pre-trained Deep Models with Data Augmentation for the ComParE 2020 Challenge Mask Task”, in Proceedings of INTERSPEECH, pp. 2047-2051, Shanghai, China, October 2020.

[3] Tomoya Koike, Kun Qian*, Qiuqiang Kong, Mark D. Plumbley, Björn W. Schuller, and. Yoshiharu Yamamoto, “Audio for Audio is Better? An Investigation on Transfer Learning Models for Heart Sound Classification”, in Proceedings of EMBC, pp. 74-77, Montréal, Canada, July 2020.

[4] Kun Qian*, Hiroyuki Kuromiya, Zhao Ren, Maximilian Schmitt, Zixing Zhang, Toru Nakamura, Kazuhiro Yoshiuchi, Björn W. Schuller, and Yoshiharu Yamamoto, “Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach”, in Proceedings of ISICDM, pp. 71-75, Best Paper Nomination Award, Xi’an, China, August 2019.

[5] Kun Qian*, Hiroyuki Kuromiya, Zixing Zhang, Jinhyuk Kim, Toru Nakamura, Kazuhiro. Yoshiuchi, Björn W. Schuller, and Yoshiharu Yamamoto, “Teaching Machines to Know Your Depressive State: On Physical Activity in Health and Major Depressive Disorder”, in Proceedings of EMBC, pp. 3592-3595, Berlin, Germany, July 2019.

中文论文

[1] 钱昆,董逢泉,任昭,戴振宇,董博,博雅恩,“心音识别的机遇与挑战:深圳心音数据库简介”,《复旦学报》(自然科学版),59 (3), 354-359.

[1] 乔玉,钱昆,赵子平,“基于机器听觉的鸟声识别的中文研究综述”,《复旦学报》(自然科学版),59 (3), 375-380.

专利

[1] System zur Ermittlung anatomischer Ursachen fuer die Entstehung von Schnarchgeraeuschen,德国,专利号:DE202016001773U12016616日,排名第2/3

[2] 基于鼾声共振峰和功率比轨迹的上气道变化监测方法,中国,专利号:ZL201310131916.62013710日,排名第2/4

[3] 基于多特征的夜间睡眠声信号分析方法,中国,专利号:ZL201310295535.12013109日,排名第2/5

[4] 一种适用于传感网络的智能被动声探测节点装置,中国,专利号:CN201310344312.X2015211日,排名第5/7

Personal Resume

Kun QIAN received his doctoral degree (Dr.-Ing.) for his study on automatic general audio signal classification in 2018 in electrical engineering and information technology from Technical University of Munich (TUM), Germany. From 2021, he has been appointed as a (Full) Professor with a title of “Teli Young Fellow” at the Beijing Institute of Technology, China. He is a Senior Member of the IEEE. He has a strong collaboration connection to prestigious universities in Germany, UK, Japan, Singapore, and the USA. Dr.Qian serves as an Associate Editor for the IEEE Transactions on Affective Computing, Frontiers in Digital Health, and BIO Integration. He (co-)authored more than 70 publications in peer reviewed journals, and conference proceedings having received more than 1 000 citations (h-index 19). We are always seeking for self-motivated students and/or internship visitors.


Education Background

10/2014 to 11/2018, Technical University of Munich/Germany, Dr.-Ing. (PhD in Engineering)

09/2011 to 04/2014, Nanjing University of Science and Technology/China, Master

09/2007 to 06/2011, Sichuan Normal University/China, Bachelor


Positions and Employment

08/2021 to present, Professor, Beijing Institute of Technology, China

09/2019 to 07/2021, JSPS Postdoctoral Research Fellow, The University of Tokyo, Japan

11/2018 to 09/2019, Project Researcher, The University of Tokyo, Japan

01/2018 to 03/2018, Visiting Scholar, Carnegie Mellon University, USA

04/2016 to 11/2016, Visiting Scholar, Tokyo Institute of Technology, Japan

11/2013 to 03/2014, Visiting Scholar, Nanyang Technological University, Singapore


Professional Membership/Academic Appointments

IEEE Senior Member, since 2021

Member of the Expert Committee of the Gesellschaft Chinesischer Informatiker in Deutschland e.V., since 2021

Associate Editor of IEEE Transactions on Affective Computing, since 2021m

Associate Editor of BIO Integration, since 2021

Associate Editor of Frontiers in Digital Health, since 2019

Chair and Leading Organiser of the Special Session on Computer Audition for Healthcare at ICASSP 2021/2022


Research and Teaching Interest

Computer Audition, Affective Computing, Artificial Intelligence for Medicine, Intelligent Signal and Information Processing, Brain Sciences


Honors and Awards

03/2021, Frontiers in Digital Health 2020 Journal Ambassador Award

11/2020, Excellent Achievement Award of the Zhejiang Lab's International Talent Fund for Young Professionals

11/2019, JSPS Postdoctoral Fellowship for Research in Japan