ItôWave: Itô Stochastic Differential Equation is all You Need for Wave Generation
By Shoule Wu, Ziqiang Shi. 2022
10 citations recorded by the source corpus.
In this paper, we propose a vocoder based on a pair of forward and reverse-time linear stochastic differential equations (SDE). The solutions of this SDE pair are two stochastic processes, one of which turns the distribution of wave, that we want to generate, into a simple and tractable distribution. The other is the generation procedure that turns this tractable simple signal into the target wave. The model is called ItôWave. It uses the Wiener process as a driver to gradually subtract the excess signal from the noise signal to generate realistic corresponding meaningful audio respectively, under the conditional inputs of original mel spectrogram. The results of the experiment show that the mean opinion scores (MOS) of ItôWave can exceed the current state-of-the-art (SOTA) methods, and reached 4.35±0.115. The generated audio samples are available online1.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
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Citations in the corpus
- ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering (2024, methodology): “Some methods, utilizing techniques such as VAE (Hsu et al., 2019; Lee et al., 2022), flow (Miao et al., 2020; Kim et al., 2020), diffusion (Jeong et al., 2021; Kim et al., 2022; Popov et al., 2021), and others (Wu and Shi, 2022), have achieved promising performance in end-to-end speech synthesis.”
- A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI (2023, unknown): “ItôWave [125] is the first to propose a vocoder based on linear Itô SDE.”