A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement

Despite noise suppression being a mature area in signal processing, it remains highly dependent on fine tuning of estimator algorithms and parameters. In this paper, we demonstrate a hybrid DSP/deep learning approach to noise suppression. We focus strongly on keeping the complexity as low as possibl...

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Bibliographic Details
Main Author: Valin, Jean-Marc
Format: Conference Proceeding
Language:English
Subjects:
Complexity theory
Gain
Microsoft Windows
Noise measurement
Noise reduction
noise suppression
recurrent neural network
Windows
Online Access:Request full text
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Summary:Despite noise suppression being a mature area in signal processing, it remains highly dependent on fine tuning of estimator algorithms and parameters. In this paper, we demonstrate a hybrid DSP/deep learning approach to noise suppression. We focus strongly on keeping the complexity as low as possible, while still achieving high-quality enhanced speech. A deep recurrent neural network with four hidden layers is used to estimate ideal critical band gains, while a more traditional pitch filter attenuates noise between pitch harmonics. The approach achieves significantly higher quality than a traditional minimum mean squared error spectral estimator, while keeping the complexity low enough for real-time operation at 48 kHz on a low-power CPU.
ISSN:2473-3628
DOI:10.1109/MMSP.2018.8547084