报告时间：4月21日，星期五，10:30-11:30,数理楼235教室

报告题目：Modal composition integrated fast-rotating sound source localization under

Bayesian framework for axial fans

摘要：The modal composition beamforming (MCB) method can quickly achieve the localization of rotating blade sound sources, however, its relatively low resolution and unclear application range lead to certain limitations in its practical application. Therefore, this study aims to investigate in depth the MCB-based high-resolution localization method for rotating multiple sound sources and apply it to the field of axial-flow fan noise reduction. The study employs both variational Bayesian approximation (VBA) and subspace variational Bayesian (SVB) methods to solve the MCBbased rotating sound source power propagation (RSP) model in a Bayesian framework. In this paper, the RSP model is experimentally validated for the first time. In addition, a pioneering parameter selection scheme is proposed in this paper by investigating the applicable boundary of the MCB method, and its effectiveness is verified by simulation. Also, for the sound source localization results, three evaluation metrics are proposed in this paper, and the processing times of the related simulations are given to assist in verifying the above scheme. The proposed method of highresolution localization of rotating sound sources in this paper is fast in calculation, effective and high in resolution, and can realize the leaf tip noise localization of multi-blade high speed axial fans. And the proposed MCB parameter selection scheme can effectively guide the acoustic measurements and the practical application of the method.

报告人简介：初宁博士，荣获国防科大信息工程学士、法国巴黎十一大学自动化信号处理硕士、信息与通信系统科学博士学位，曾任瑞士洛桑联邦理工合作研究员，现任上专股份首席研究员、副总工 IEEE 高级会员、浙江省声学学会常务理事长。长期从事贝叶斯推断方法，多传感器信息融合，在国际上率先研发工业芯肺系统，推动传统通风装备向绿色智能化转型，荣获国家四部委智能制造优秀案例、浙江省工业互联网平台。近5年发表高水平SCI论文32篇，以第一完成人授权发明专利28项、国际专利2项、软著6项，荣获中国职责安康协会科技进步二等奖(排2)、国际日内瓦发明银奖、国际数模竞赛一等奖、全国数模竞赛一等奖(2次)、全国节能减排大学一等奖。