Search Results for author: Yin-Jyun Luo

Found 6 papers, 4 papers with code

Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio

1 code implementation12 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.

Data Augmentation Disentanglement +1

Omnizart: A General Toolbox for Automatic Music Transcription

1 code implementation1 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).

Chord Recognition Information Retrieval +3

The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy

1 code implementation20 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.

Music Transcription

Generative Modelling for Controllable Audio Synthesis of Expressive Piano Performance

1 code implementation16 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.

Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders

no code implementations19 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.

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