
This research proposes a novel sampling algorithm for speech super-resolution that leverages the power of variational diffusion models (VDMs) to reconstruct high-resolution speech from low-resolution audio, achieving state-of-the-art results and demonstrating robustness against different downsampling methods.


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a-novel-sampling-algorithm-for-speech-super-resolution-using-variational-diffusion-models


A Novel Sampling Algorithm for Speech Super-Resolution Using Variational Diffusion Models


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Wave-U-Mamba is an efficient and effective end-to-end framework for speech super-resolution that directly generates high-resolution speech waveforms from low-resolution inputs, outperforming existing state-of-the-art models.


wave-u-mamba-an-efficient-and-high-quality-end-to-end-framework-for-speech-super-resolution


Wave-U-Mamba: An Efficient and High-Quality End-to-End Framework for Speech Super-Resolution
