3 citations · 3 across the 5 of their papers we have counts for
5 papers
LargeSHS: A large-scale dataset of music adaptation
Chih-Pin Tan, Hsuan-Kai Kao, Li Su +1
Recent advances in AI-based music generation have focused heavily on text-conditioned models, with less attention given to reference-based generation such as song adaptation. To su…
Segment-Factorized Full-Song Generation on Symbolic Piano Music
Ping-Yi Chen, Chih-Pin Tan, Yi-Hsuan Yang
We propose the Segmented Full-Song Model (SFS) for symbolic full-song generation. The model accepts a user-provided song structure and an optional short seed segment that anchors t…
Time-Shifted Token Scheduling for Symbolic Music Generation
Ting-Kang Wang, Chih-Pin Tan, Yi-Hsuan Yang
Symbolic music generation faces a fundamental trade-off between efficiency and quality. Fine-grained tokenizations achieve strong coherence but incur long sequences and high comple…
PiCoGen2: Piano cover generation with transfer learning approach and weakly aligned data
Chih-Pin Tan, Hsin Ai, Yi-Hsin Chang +2
Piano cover generation aims to create a piano cover from a pop song. Existing approaches mainly employ supervised learning and the training demands strongly-aligned and paired song…
PiCoGen: Generate Piano Covers with a Two-stage Approach
Chih-Pin Tan, Shuen-Huei Guan, Yi-Hsuan Yang
Cover song generation stands out as a popular way of music making in the music-creative community. In this study, we introduce Piano Cover Generation (PiCoGen), a two-stage approac…