3 citations · 4 across the 6 of their papers we have counts for
6 papers
DisMix: Disentangling Mixtures of Musical Instruments for Source-level Pitch and Timbre Manipulation
Yin-Jyun Luo, Kin Wai Cheuk, Woosung Choi +8
Existing work on pitch and timbre disentanglement has been mostly focused on single-instrument music audio, excluding the cases where multiple instruments are presented. To fill th…
Improving Unsupervised Clean-to-Rendered Guitar Tone Transformation Using GANs and Integrated Unaligned Clean Data
Yu-Hua Chen, Woosung Choi, Wei-Hsiang Liao +5
Recent years have seen increasing interest in applying deep learning methods to the modeling of guitar amplifiers or effect pedals. Existing methods are mainly based on the supervi…
MR-MT3: Memory Retaining Multi-Track Music Transcription to Mitigate Instrument Leakage
Hao Hao Tan, Kin Wai Cheuk, Taemin Cho +2
This paper presents enhancements to the MT3 model, a state-of-the-art (SOTA) token-based multi-instrument automatic music transcription (AMT) model. Despite SOTA performance, MT3 h…
Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training
Kin Wai Cheuk, Keunwoo Choi, Qiuqiang Kong +5
In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from…
Jointist: Joint Learning for Multi-instrument Transcription and Its Applications
Kin Wai Cheuk, Keunwoo Choi, Qiuqiang Kong +5
In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from…
Danna-Sep: Unite to separate them all
Chin-Yun Yu, Kin-Wai Cheuk
Deep learning-based music source separation has gained a lot of interest in the last decades. Most of the existing methods operate with either spectrograms or waveforms. Spectrogra…