Machine learning for music: DJ systems, music generation, drum transcription.
Sep 2026 — DJustify code is out on GitHub×Jul 2026 — Separate-and-Detect accepted to ISMIR 2026×Sep 2026 — DJustify code is out on GitHub×Jul 2026 — Separate-and-Detect accepted to ISMIR 2026×
Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks
A GAN with a differentiable DJ mixer that learns fader and EQ curves from real DJ mixes. My part was the DJ side: domain knowledge and the training data.
Bo-Yu Chen, Wei-Han Hsu, Wei-Hsiang Liao, Marco A. Martínez Ramírez, Yuki Mitsufuji, Yi-Hsuan Yang
I'm a PhD student in the Data Science Degree Program at National Taiwan University and Academia Sinica, working with Li Su and Yi-Hsuan Yang. My research is machine learning for music, on both the listening side and the making side.
Lately that has meant a lot of DJ work on pop, where the vocals make everything harder, and before that drum transcription and source separation with latent diffusion.
Before the PhD I worked on electronic dance music classification at Academia Sinica, and as a machine learning engineer at CloudMile, on customer lifetime value models and LLM projects.
After hours
I DJ for fun. I also play a lot of basketball: school teams at NSYSU and NTPU, and a few amateur teams on weekends. Before that it was soccer, on the school team through junior high.