5 citations · 15 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…