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Open AccessArticle

A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector

School of Electronic and Information Engineering, Tianjin University, Tianjin 300072, China
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Appl. Sci. 2020, 10(7), 2218; https://doi.org/10.3390/app10072218
Received: 1 February 2020 / Revised: 12 March 2020 / Accepted: 18 March 2020 / Published: 25 March 2020
(This article belongs to the Collection Recent Applications of Active and Passive Noise Control)
This paper presents a unified speech enhancement system to remove both background noise and interfering speech in serious noise environments by jointly utilizing the parabolic reflector model and neural beamformer. First, the amplification property of paraboloid is discussed, which significantly improves the Signal-to-Noise Ratio (SNR) of a desired signal. Therefore, an appropriate paraboloid channel is analyzed and designed through the boundary element method. On the other hand, a time-frequency masking approach and a mask-based beamforming approach are discussed and incorporated in an enhancement system. It is worth noticing that signals provided by the paraboloid and the beamformer are exactly complementary. Finally, these signals are employed in a learning-based fusion framework to further improve the system performance in low SNR environments. Experiments demonstrate that our system is effective and robust in five different noisy conditions (speech interfered with factory, pink, destroyer engine, volvo, and babble noise), as well as in different noise levels. Compared with the original noisy speech, significant average objective metrics improvements are about Δ STOI = 0.28, Δ PESQ = 1.31, Δ fwSegSNR = 11.9. View Full-Text
Keywords: speech enhancement; parabolic reflector; microphone array; deep neural network; beamformer speech enhancement; parabolic reflector; microphone array; deep neural network; beamformer
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Zhang, T.; Geng, Y.; Sun, J.; Jiao, C.; Ding, B. A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector. Appl. Sci. 2020, 10, 2218.

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