arXiv:2202.07790
공식 코드 · Papers with Code
Speech Denoising in the Waveform Domain with Self-Attention
원문 초록
In this work, we present CleanUNet, a causal speech denoising model on the raw waveform. The proposed model is based on an encoder-decoder architecture combined with several self-attention blocks to refine its bottleneck representations, which is crucial to obtain good results. The model is optimized through a set of losses defined over both waveform and multi-resolution spectrograms. The proposed method outperforms the state-of-the-art models in terms of denoised speech quality from various objective and subjective evaluation metrics. We release our code and models at https://github.com/nvidia/cleanunet.
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EDNet: A Distortion-Agnostic Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training
A Comparative Evaluation of Deep Learning Models for Speech Enhancement in Real-World Noisy Environments
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DiffS-NOCS: 3D Point Cloud Reconstruction through Coloring Sketches to NOCS Maps Using Diffusion Models
ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering
D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer
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