activity
20172020
most citedLearning Disentangled Representations for Timber and Pitch in Music Audio

13 citations · 45 across the 9 of their papers we have counts for

collaborators

9 papers

cs.SD2020★ 7 cited

Automatic Melody Harmonization with Triad Chords: A Comparative Study

Yin-Cheng Yeh, Wen-Yi Hsiao, Satoru Fukayama +7

Several prior works have proposed various methods for the task of automatic melody harmonization, in which a model aims to generate a sequence of chords to serve as the harmonic ac…

eess.AS2019★ 5 cited

Musical Composition Style Transfer via Disentangled Timbre Representations

Yun-Ning Hung, I-Tung Chiang, Yi-An Chen +1

Music creation involves not only composing the different parts (e.g., melody, chords) of a musical work but also arranging/selecting the instruments to play the different parts. Wh…

cs.SD2018★ 13 cited

Learning Disentangled Representations for Timber and Pitch in Music Audio

Yun-Ning Hung, Yi-An Chen, Yi-Hsuan Yang

Timbre and pitch are the two main perceptual properties of musical sounds. Depending on the target applications, we sometimes prefer to focus on one of them, while reducing the eff…

cs.SD2018

Multitask learning for frame-level instrument recognition

Yun-Ning Hung, Yi-An Chen, Yi-Hsuan Yang

For many music analysis problems, we need to know the presence of instruments for each time frame in a multi-instrument musical piece. However, such a frame-level instrument recogn…

cs.MM2018★ 1 cited

Representation Learning for Image-based Music Recommendation

Chih-Chun Hsia, Kwei-Herng Lai, Yian Chen +2

Image perception is one of the most direct ways to provide contextual information about a user concerning his/her surrounding environment; hence images are a suitable proxy for con…

cs.IR2018★ 2 cited

Superhighway: Bypass Data Sparsity in Cross-Domain CF

Kwei-Herng Lai, Ting-Hsiang Wang, Heng-Yu Chi +3

Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains. Many traditional methods foc…