3 citations · 3 across the 3 of their papers we have counts for
8 papers · 1 filter
Understanding Audio Features via Trainable Basis Functions
Kwan Yee Heung, Kin Wai Cheuk, Dorien Herremans
In this paper we explore the possibility of maximizing the information represented in spectrograms by making the spectrogram basis functions trainable. We experiment with two diffe…
ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data
Kin Wai Cheuk, Dorien Herremans, Li Su
Most of the current supervised automatic music transcription (AMT) models lack the ability to generalize. This means that they have trouble transcribing real-world music recordings…
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…
The impact of Audio input representations on neural network based music transcription
Kin Wai Cheuk, Kat Agres, Dorien Herremans
This paper thoroughly analyses the effect of different input representations on polyphonic multi-instrument music transcription. We use our own GPU based spectrogram extraction too…
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…