Cheuk K.W., Sawata R, Uesaka T, Murata N, Takahashi N, Takahashi S, Herremans D., Mitsufuji Y.  2023.  DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability. ICASSP.

• Google Scholar
• BibTeX

diffroll.pdf (2.2 MB)

Koh E., Cheuk K.W., Heung K.Y., Agres K., Herremans D..  2023.  MERP: A Music Dataset with Emotion Ratings and Raters’ Profile Information. Sensors - Intelligent Sensors. 23(1)

• Google Scholar
• BibTeX

sensors-23-00382 (2).pdf (1.21 MB)

Cheuk K.W., Choi K., Kong Q., Li B., Won M., Hung A., Wang J.-C., Herremans D..  2022.  Jointist: Joint Learning for Multi-instrument Transcription and Its Applications.

• Google Scholar
• BibTeX

2206.10805.pdf (427.51 KB)

Kwan Y.H., Cheuk K.W., Herremans D..  2022.  Understanding Audio Features via Trainable Basis Functions. Arxiv preprint.

• Google Scholar
• BibTeX

2204.11437.pdf (7.36 MB)

Cheuk K.W., Luo Y.J., Benetos E., Herremans D..  2021.  The Effect of Spectrogram Reconstructions on Automatic Music Transcription:An Alternative Approach to Improve Transcription Accuracy. Proceedings of the International Conference on Pattern Recognition (ICPR2020).

• Google Scholar
• BibTeX

2010.09969.pdf (3.46 MB)

Cheuk K.W., Su L., Herremans D..  2021.  ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data. ACM Multimedia.

• Google Scholar
• BibTeX

Cheuk K.W., Luo Y.J., Benetos E., Herremans D..  2021.  Revisiting the Onsets and Frames Model with Additive Attention. Proceedings of the International Joint Conference on Neural Networks (IJCNN).

• Google Scholar
• BibTeX

2104.06607.pdf (1.52 MB)

Cheuk K.W., Agres K., Herremans D..  2020.  The impact of Audio input representations on neural network based music transcription. Proceedings of the International Joint Conference on Neural Networks (IJCNN).

• Google Scholar
• BibTeX

2001.09989.pdf (1.87 MB)

Cheuk K.W., Anderson H., Agres K., Herremans D..  2020.  nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks. IEEE Access.

• Google Scholar
• BibTeX

nnAudio.pdf (10.2 MB)

Cheuk K.W., Luo Y.J., BT B, Roig G., Herremans D..  2020.  Regression-based music emotion prediction using triplet neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN).

• Google Scholar
• BibTeX

2001.09988.pdf (777.31 KB)

Luo Y.J., Cheuk K.W., Nakano T., Goto M., Herremans D..  2020.  Unsupervised disentanglement of pitch and timbre for isolated musical instrument sounds. Proceedings of the International Society of Music Information Retrieval (ISMIR).

• Google Scholar
• BibTeX

Cheuk K.W., BT B, Roig G., Herremans D..  2019.  Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019).

• Google Scholar
• BibTeX

1910.01463.pdf (934.76 KB)

Cheuk K.W., Agres K., Herremans D..  2019.  nnAudio: A PyTorch Audio Processing Tool Using 1D Convolution neural networks. ISMIR - Late Breaking Demo.

• Google Scholar
• BibTeX

nnAudio.pdf (399.08 KB)

Cheuk K.W., BT B, Roig G., Herremans D..  2018.  Blacklisted speaker identification using triplet neural networks. MCE2018 competition.

• Google Scholar
• BibTeX

SUTD_description.pdf (133.08 KB)