5 citations · 15 across the 8 of their papers we have counts for
10 papers
Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio
Yin-Jyun Luo, Sebastian Ewert, Simon Dixon
Disentangled sequential autoencoders (DSAEs) represent a class of probabilistic graphical models that describes an observed sequence with dynamic latent variables and a static late…
Omnizart: A General Toolbox for Automatic Music Transcription
Yu-Te Wu, Yin-Jyun Luo, Tsung-Ping Chen +4
We present and release Omnizart, a new Python library that provides a streamlined solution to automatic music transcription (AMT). Omnizart encompasses modules that construct the l…
Revisiting the Onsets and Frames Model with Additive Attention
Kin Wai Cheuk, Yin-Jyun Luo, Emmanouil Benetos +1
Recent advances in automatic music transcription (AMT) have achieved highly accurate polyphonic piano transcription results by incorporating onset and offset detection. The existin…
Toward Expressive Singing Voice Correction: On Perceptual Validity of Evaluation Metrics for Vocal Melody Extraction
Yin-Jyun Luo, Yuen-Jen Lin, Li Su
Singing voice correction (SVC) is an appealing application for amateur singers. Commercial products automate SVC by snapping pitch contours to equal-tempered scales, which could le…
The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy
Kin Wai Cheuk, Yin-Jyun Luo, Emmanouil Benetos +1
Most of the state-of-the-art automatic music transcription (AMT) models break down the main transcription task into sub-tasks such as onset prediction and offset prediction and tra…
Generative Modelling for Controllable Audio Synthesis of Expressive Piano Performance
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…
Regression-based music emotion prediction using triplet neural networks
Kin Wai Cheuk, Yin-Jyun Luo, Balamurali B +3
In this paper, we adapt triplet neural networks (TNNs) to a regression task, music emotion prediction. Since TNNs were initially introduced for classification, and not for regressi…
Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders
Yin-Jyun Luo, Chin-Chen Hsu, Kat Agres +1
We propose a flexible framework that deals with both singer conversion and singers vocal technique conversion. The proposed model is trained on non-parallel corpora, accommodates m…
Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders
Yin-Jyun Luo, Kat Agres, Dorien Herremans
In this paper, we learn disentangled representations of timbre and pitch for musical instrument sounds. We adapt a framework based on variational autoencoders with Gaussian mixture…
Singing voice correction using canonical time warping
Yin-Jyun Luo, Ming-Tso Chen, Tai-Shih Chi +1
Expressive singing voice correction is an appealing but challenging problem. A robust time-warping algorithm which synchronizes two singing recordings can provide a promising solut…