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Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data

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Document pages: 5 pages

Abstract: Data-driven speech enhancement employing deep neural networks (DNNs) canprovide state-of-the-art performance even in the presence of non-stationarynoise. During the training process, most of the speech enhancement neuralnetworks are trained in a fully supervised way with losses requiring noisyspeech to be synthesized by clean speech and additive noise. However, in a realimplementation, only the noisy speech mixture is available, which leads to thequestion, how such data could be advantageously employed in training. In thiswork, we propose an end-to-end non-intrusive PESQNet DNN which estimatesperceptual evaluation of speech quality (PESQ) scores, allowing areference-free loss for real data. As a further novelty, we combine the PESQNetloss with denoising and dereverberation loss terms, and train a complexmask-based fully convolutional recurrent neural network (FCRN) in a "weakly "supervised way, each training cycle employing some synthetic data, some realdata, and again synthetic data to keep the PESQNet up-to-date. In a subjectivelistening test, our proposed framework outperforms the Interspeech 2021 DeepNoise Suppression (DNS) Challenge baseline overall by 0.09 MOS points and inparticular by 0.45 background noise MOS points.

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