2 citations · 2 across the 6 of their papers we have counts for
6 papers
D3PIA: A Discrete Denoising Diffusion Model for Piano Accompaniment Generation From Lead sheet
Eunjin Choi, Hounsu Kim, Hayeon Bang +2
Generating piano accompaniments in the symbolic music domain is a challenging task that requires producing a complete piece of piano music from given melody and chord constraints,…
On the de-duplication of the Lakh MIDI dataset
Eunjin Choi, Hyerin Kim, Jiwoo Ryu +2
A large-scale dataset is essential for training a well-generalized deep-learning model. Most such datasets are collected via scraping from various internet sources, inevitably intr…
PianoBind: A Multimodal Joint Embedding Model for Pop-piano Music
Hayeon Bang, Eunjin Choi, Seungheon Doh +1
Solo piano music, despite being a single-instrument medium, possesses significant expressive capabilities, conveying rich semantic information across genres, moods, and styles. How…
AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation
Gyehun Go, Satbyul Han, Ahyeon Choi +3
Recent advances in text-to-music (TTM) generation have enabled controllable and expressive music creation using natural language prompts. However, the emotional fidelity of TTM sys…
PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text
Hayeon Bang, Eunjin Choi, Megan Finch +4
While piano music has become a significant area of study in Music Information Retrieval (MIR), there is a notable lack of datasets for piano solo music with text labels. To address…
YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations
Eunjin Choi, Yoonjin Chung, Seolhee Lee +3
Existing multi-instrumental datasets tend to be biased toward pop and classical music. In addition, they generally lack high-level annotations such as emotion tags. In this paper,…