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NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling
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In this work, we introduce NU-Wave, the first neural audio upsampling model to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs, while prior works could generate only up to 16kHz. NU-Wave is the first diffusion probabilistic model for audio super-resolution which is engineered based on neural vocoders. NU-Wave generates high-quality audio that achieves high performance in terms of signal-to-noise ratio (SNR), log-spectral distance (LSD), and accuracy of the ABX test. In all cases, NU-Wave outperforms the baseline models despite the substantially smaller model capacity (3.0M parameters) than baselines (5.4-21%). The audio samples of our model are available at https://mindslab-ai.github.io/nuwave, and the code will be made available soon.
Forward citations
Cited by 2 Pith papers
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Semantic Sampling via Learnable Observation Front Ends
Learnable acoustic filterbanks plus constrained mixing and temporal readout produce more informative low-rate observations for speech reconstruction than fixed waveform sampling at the same budget.
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Inference-time Scaling for Diffusion-based Audio Super-resolution
Generating 120 candidate super-resolved audios and choosing the best by task-specific verifiers improves speech, music, and sound effects over single-sample diffusion output, at 120x compute.
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