Kin Wai Cheuk

Orcid: 0000-0003-3213-8242

According to our database1, Kin Wai Cheuk authored at least 30 papers between 2019 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis.
CoRR, May, 2026

SteerMusic: Enhanced Musical Consistency for Zero-shot Text-Guided and Personalized Music Editing.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Automatic Music Mixing using a Generative Model of Effect Embeddings.
CoRR, November, 2025

Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution.
CoRR, July, 2025

Large-Scale Training Data Attribution for Music Generative Models via Unlearning.
CoRR, June, 2025

Music Foundation Model as Generic Booster for Music Downstream Tasks.
Trans. Mach. Learn. Res., 2025

Latent Diffusion Bridges for Unsupervised Musical Audio Timbre Transfer.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

Variable Bitrate Residual Vector Quantization for Audio Coding.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

2024
VRVQ: Variable Bitrate Residual Vector Quantization for Audio Compression.
CoRR, 2024

Latent Diffusion Bridges for Unsupervised Musical Audio Timbre Transfer.
CoRR, 2024

DisMix: Disentangling Mixtures of Musical Instruments for Source-level Pitch and Timbre Manipulation.
CoRR, 2024

Improving Unsupervised Clean-to-Rendered Guitar Tone Transformation Using GANs and Integrated Unaligned Clean Data.
CoRR, 2024

MR-MT3: Memory Retaining Multi-Track Music Transcription to Mitigate Instrument Leakage.
CoRR, 2024

Timbre-Trap: A Low-Resource Framework for Instrument-Agnostic Music Transcription.
Proceedings of the IEEE International Conference on Acoustics, 2024

2023
MERP: A Music Dataset with Emotion Ratings and Raters' Profile Information.
Sensors, 2023

Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training.
CoRR, 2023

Diffroll: Diffusion-Based Generative Music Transcription with Unsupervised Pretraining Capability.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
Jointist: Joint Learning for Multi-instrument Transcription and Its Applications.
CoRR, 2022

Understanding Audio Features via Trainable Basis Functions.
CoRR, 2022

2021
Danna-Sep: Unite to separate them all.
CoRR, 2021

ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data.
Proceedings of the MM '21: ACM Multimedia Conference, Virtual Event, China, October 20, 2021

Revisiting the Onsets and Frames Model with Additive Attention.
Proceedings of the International Joint Conference on Neural Networks, 2021

2020
nnAudio: An on-the-Fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks.
IEEE Access, 2020

Unsupervised Disentanglement of Pitch and Timbre for Isolated Musical Instrument Sounds.
Proceedings of the 21th International Society for Music Information Retrieval Conference, 2020

Regression-based Music Emotion Prediction using Triplet Neural Networks.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

The Impact of Audio Input Representations on Neural Network based Music Transcription.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

2019
nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks.
CoRR, 2019

Untangling indices of emotion in music using neural networks.
Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Latent Space Representation for Multi-Target Speaker Detection and Identification with a Sparse Dataset Using Triplet Neural Networks.
Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop, 2019


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