1 code implementation • 12 May 2022 • Yin-Jyun Luo, Sebastian Ewert, Simon Dixon
In this paper, we show that the vanilla DSAE suffers from being sensitive to the choice of model architecture and capacity of the dynamic latent variables, and is prone to collapse the static latent variable.
1 code implementation • 1 Jun 2021 • Yu-Te Wu, Yin-Jyun Luo, Tsung-Ping Chen, I-Chieh Wei, Jui-Yang Hsu, Yi-Chin Chuang, Li Su
We present and release Omnizart, a new Python library that provides a streamlined solution to automatic music transcription (AMT).
2 code implementations • 20 Oct 2020 • Kin Wai Cheuk, Yin-Jyun Luo, Emmanouil Benetos, Dorien Herremans
We attempt to use only the pitch labels (together with spectrogram reconstruction loss) and explore how far this model can go without introducing supervised sub-tasks.
1 code implementation • 16 Jun 2020 • Hao Hao Tan, Yin-Jyun Luo, Dorien Herremans
We present a controllable neural audio synthesizer based on Gaussian Mixture Variational Autoencoders (GM-VAE), which can generate realistic piano performances in the audio domain that closely follows temporal conditions of two essential style features for piano performances: articulation and dynamics.
no code implementations • 3 Dec 2019 • Yin-Jyun Luo, Chin-Chen Hsu, Kat Agres, Dorien Herremans
We propose a flexible framework that deals with both singer conversion and singers vocal technique conversion.
no code implementations • 19 Jun 2019 • Yin-Jyun Luo, Kat Agres, Dorien Herremans
Specifically, we use two separate encoders to learn distinct latent spaces for timbre and pitch, which form Gaussian mixture components representing instrument identity and pitch, respectively.