Research Topics
Our research focuses on applying computational methods such as digitial signal processing and machine learning to various musical contexts including music listening, performance, composition, production, education, entertainment and arts.
Music Performance

Piano Performance Analysis
Polyphonic Piano Transcription, Audio-to-Score Alignment, Performer Identification, Performer Motion Analysis
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Singing Analysis and Style Transfer
Singing Note Transcription, Singing Technique Analysis, Singing Style Transfer


Sound-to-Motion and Motion-to-Sound
LipSync Generation
Music Composition and Production

Intelligent Music Production
Drum Sample Retrieval, Automatic Parameter Estimation of Digital Audio Effects


Symbolic Music Generation
Game BGM Generation, Drum Pattern Generation and Expressive Control, Piano Music Generation
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Korean Traditional Music
Music Listening

Deep Audio Embedding Learning for Music
Disentangled Representation Learning, Representation Learning with Meta Data
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Pop Music Vocal Analysis
Semantic Vocal Tagging, Singer identification, Cross-domain Embedding Space Learning
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Vocal Melody Extraction and Transcription
Vocal Melody Extraction, Singing Voice Detection, Singing Note Transcription
More
Symbolic Melody Similarity
Symbolic Music Representation, Symbolic Melody Retrieval

Multimodal Music Retrieval
Musical Word Embedding, Zero-shot Learning for Music Annotation and Retrieval, Query-by-Word, Query-by-Image
MoreArts

Soundscape and AI
Neuroscape, Mixedscape