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Speech super-resolution (SSR) aims to predict a high resolution (HR) speech
signal from its low resolution (LR) corresponding part. Most neural SSR models
focus on producing the final result in a noise-free environment by recovering
the spectrogram of high-frequency part of the signal and concatenating it with
the original low-frequency part. Although these methods achieve high accuracy,
they become less effective when facing the real-world scenario, where
unavoidable noise is present. To address this problem, we propose a Super
Denoise Net (SDNet), a neural network for a joint task of super-resolution and
noise reduction from a low sampling rate signal. To that end, we design gated
convolution and lattice convolution blocks to enhance the repair capability and
capture information in the time-frequency axis, respectively. The experiments
show our method outperforms baseline speech denoising and SSR models on DNS
2020 no-reverb test set with higher objective and subjective scores.
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