Discrete Optimal Transport and Voice Conversion

In this work, we address the voice conversion (VC) task using a vector-based interface. To align audio embeddings between speakers, we employ discrete optimal transport mapping. Our evaluation results demonstrate the high quality and effectiveness of this method. Additionally, we show that applying discrete optimal transport as a post-processing step in audio generation can lead to the incorrect classification of synthetic audio as real.

Music Source Separation with Generative Flow

Full supervision models for source separation are trained on mixture-source parallel data and have achieved superior performance in recent years. However, large-scale and naturally mixed parallel training data are difficult to obtain for music, and such models are difficult to adapt to mixtures with new sources. Source-only supervision models, in contrast, only require clean sources for training; They learn source models and then apply these models to separate the mixture.