40 citations · 96 across the 15 of their papers we have counts for
16 papers
Pitch Smoothing Using Relative Interval Networks
Chin-Yun Yu, Chi-Jen Peng, Li Su +1
Pitch tracking systems typically couple a per-frame fundamental frequency () estimator with a temporal smoothing stage to obtain continuous trajectories. Conventional Viterbi…
Separate-and-Detect: Unified Drum Transcription and Stem Generation via Latent Diffusion
Wei-Han Hsu, Chih-Cheng Chang, Bo-Yu Chen +2
Automatic Drum Transcription (ADT) is commonly formulated as a direct mapping from a music mixture to symbolic drum events. While effective for transcription, this formulation disc…
A Phoneme-Informed Neural Network Model for Note-Level Singing Transcription
Sangeon Yong, Li Su, Juhan Nam
Note-level automatic music transcription is one of the most representative music information retrieval (MIR) tasks and has been studied for various instruments to understand music.…
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…
Omnizart: A General Toolbox for Automatic Music Transcription
Yu-Te Wu, Yin-Jyun Luo, Tsung-Ping Chen +4
We present and release Omnizart, a new Python library that provides a streamlined solution to automatic music transcription (AMT). Omnizart encompasses modules that construct the l…
Toward Expressive Singing Voice Correction: On Perceptual Validity of Evaluation Metrics for Vocal Melody Extraction
Yin-Jyun Luo, Yuen-Jen Lin, Li Su
Singing voice correction (SVC) is an appealing application for amateur singers. Commercial products automate SVC by snapping pitch contours to equal-tempered scales, which could le…