Search Results for author:
Found 3 papers, 3 papers with code
PhaseAug: A Differentiable Augmentation for Speech Synthesis to Simulate One-to-Many Mapping
Previous generative adversarial network (GAN)-based neural vocoders are trained to reconstruct the exact ground truth waveform from the paired mel-spectrogram and do not consider the one-to-many relationship of speech synthesis.
NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates
Conventionally, audio super-resolution models fixed the initial and the target sampling rates, which necessitate the model to be trained for each pair of sampling rates.
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
