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Your search keyword '"Matthias Grundmann"' showing total 129 results
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129 results on '"Matthias Grundmann"'

38. MediaPipe: A Framework for Building Perception Pipelines.

Author

Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, and Matthias Grundmann

Published
2019

45. Guided Speech Enhancement Network

Author

Yang Yang, Shao-Fu Shih, Hakan Erdogan, Jamie Menjay Lin, Chehung Lee, Yunpeng Li, George Sung, and Matthias Grundmann

Subjects
FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Machine Learning, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Sound, Machine Learning (cs.LG), Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

High quality speech capture has been widely studied for both voice communication and human computer interface reasons. To improve the capture performance, we can often find multi-microphone speech enhancement techniques deployed on various devices. Multi-microphone speech enhancement problem is often decomposed into two decoupled steps: a beamformer that provides spatial filtering and a single-channel speech enhancement model that cleans up the beamformer output. In this work, we propose a speech enhancement solution that takes both the raw microphone and beamformer outputs as the input for an ML model. We devise a simple yet effective training scheme that allows the model to learn from the cues of the beamformer by contrasting the two inputs and greatly boost its capability in spatial rejection, while conducting the general tasks of denoising and dereverberation. The proposed solution takes advantage of classical spatial filtering algorithms instead of competing with them. By design, the beamformer module then could be selected separately and does not require a large amount of data to be optimized for a given form factor, and the network model can be considered as a standalone module which is highly transferable independently from the microphone array. We name the ML module in our solution as GSENet, short for Guided Speech Enhancement Network. We demonstrate its effectiveness on real world data collected on multi-microphone devices in terms of the suppression of noise and interfering speech., Accepted to ICASSP 2023

Published
2023

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