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  讲座题目:Acoustic Signal Processing Based on Deep Neural Networks

  主讲嘉宾:Prof. Chin-Hui Lee(School of Electrical and Computer Engineering, Georgia Institute of Technology)

  时 间:10月15日周三下午2:00-3:30

  地 点:科大西区教学楼3C124教室

  主办单位:语音及语言信息处理国家工程实验室

  Abstract:

  In contrast to conventional model-based acoustic signal processing, we formulate a given acoustic signal processing problem in a novel deep learning framework as finding a mapping function between the observed signal and the desired targets. Monte Carlo techniques are often required to generate a large collection of signal pairs in order to learn the often-complicated structure of the a mapping functions. In the case of speech enhancement, to be able to handle a wide range of additive noises in real-world situations, a large training set, encompassing many possible combinations of speech and noise types, is first designed. Next deep neural network (DNN) architectures are employed as nonlinear regression functions to ensure a powerful approximation capability. In the case of source separation a similar simulation methodology can also be adopted. In the case of speech bandwidth expansion, the target wideband signals can be filtered and down-sampled to create the needed narrowband training examples. Finally in the case of acoustic de-reverberation, a wide variety of simulated room impulse responses are needed to generate a good training set.

  When reconstructing the desired target signals, some additional techniques may be required. For example, noisy or missing phase information may need to be estimated in order to enhance the quality of the synthesized signals. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the conventional techniques in speech enhancement, speech source separation and bandwidth expansion. It is also interesting to observe that the proposed DNN approach can also serve as an acoustic preprocessing front-end for robust speech recognition to improve performance with or without post-processing.

  Biography:

  Chin-Hui Lee is a professor at School of Electrical and Computer Engineering, Georgia Institute of Technology. Before joining academia in 2001, he had 20 years of industrial experience ending in Bell Laboratories, Murray Hill, New Jersey, as a Distinguished Member of Technical Staff and Director of the Dialogue Systems Research Department. Dr. Lee is a Fellow of the IEEE and a Fellow of ISCA. He has published over 400 papers and 30 patents, and was highly cited for his original contributions with an h-index of 66. He received numerous awards, including the Bell Labs President's Gold Award in 1998. He won the SPS's 2006 Technical Achievement Award for "Exceptional Contributions to the Field of Automatic Speech Recognition". In 2012 he was invited by ICASSP to give a plenary talk on the future of speech recognition. In the same year he was awarded the ISCA Medal in scientific achievement for “pioneering and seminal contributions to the principles and practice of automatic speech and speaker recognition”.