報告題目：Energy-Aware Multichannel Noise Reduction in Wireless Acoustic Sensor Networks (面向無線聲學傳感器網絡的多通道語音增強)
報告人：張結 博士
單位：北京大學
報告時間：2019年4月3日（周三）下午14:00
報告地點：翡翠科教樓A座1106
報告人簡介：張結，受國家留學基金委資助公派于荷蘭代爾夫特理工大學攻讀博士學位。研究方向主要是：語音增強、聲源定位、雙耳聲學、傳感器網絡、凸優(yōu)化等。擔任IEEE/ACM TASLP、IEEE Trans. SignalProcessing, IEEE Communication/Signal Processing Letters、ICASSP、ICRA、IROS等國際刊物的審稿人。目前已發(fā)表學術論文十余篇，主要發(fā)表于IEEE/ACM TASLP、IEEE Trans. SignalProcessing，并獲得2018 10th IEEE SensorArray and Multichannel Signal Processing Workshop (SAM)最佳論文獎，申請并授權國家發(fā)明專利2項。
Jie Zhang is pursuing his PhD at Delft University of Technology (TU Delft) in Netherlands,funded by the China Scholarship Council. His research interests cover multichannel speech enhancement, source localization, binaural auditory, wireless sensor networks, convex
optimization, etc. He serves as a reviewer for many international conferences/journals, e.g.,IEEE/ACM TASLP, IEEE Signal Processing Letters, IEEE Communication Letters, ICASSP,ICRA,IROS, etc. He has published more than 10 academic articles. He received the Best Student Paper Award from 2018 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). He holds two national patents.
摘要：近些年，無限聲學傳感器設備的使用越來越普遍，我們無時不處在一個大規(guī)模傳感器網絡里面。相對于傳統(tǒng)的麥克風陣列，無線聲學傳感器網絡具有很多優(yōu)勢，比如不受限于應用平臺的體積、易于布局、空間感知范圍更廣等。同時，也給語音信號處理帶來了新的挑戰(zhàn)，比如網絡資源(電池能量、通信帶寬)消耗、時變的拓撲結構、設備時間同步等。對此，我們首先提出了如何選擇能量消耗最低的麥克風子集來達到指定的語音降噪效果；其次，由于每個設備在傳輸數據之前要經過量化，我們提出了如何最優(yōu)地分配網絡量化速率；再次，考慮到中心化網絡魯棒性差的問題，我們提出了分布式語音降噪算法；最后，由于傳統(tǒng)基于波束形成的語音降噪算法需要用到聲學傳遞函數，我們提出了一種低通信速率場景下的聲學傳遞函數估計方法。
Abstract: Nowadays, wireless acoustic devices are more and more often-used in our daily life, e.g., smartphones, laptops, hand-free telephony kits, hearing aids, resulting in a large-scale wireless acoustic sensor network (WASN). Compared to the conventional microphone arrays, the so-called WASN has many advantages, e.g., the devices can be placed at any locations where it might be difficult to position wired microphones, a larger acoustic scene can be sampled. Meanwhile, there are also some new challenges that need to be addressed in the context of WASNs, e.g., resource consumption, time-varying network topology, synchronization, etc. For these, we first proposed a sensor selection strategy, which is obtained by minimizing the total transmission power over the WASN subject to a constraint on an expected noise reduction performance. Second, we proposed to distribute the communication rates to the sensors, which are used for quantizing the raw sensor measurements. Third, to improve the robustness against the network variation, we proposed a distributed noise reduction algorithm. Finally, since the traditional beamforming based noise reduction algorithms are based on the acoustic transfer function (ATF), we proposed a method for estimating this ATF at low communication rates.
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