Music and Audio Computing Lab

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.



Piano Performance Analysis and Generation

Piano Music Transcription

Piano Note Transcription, Note Intensity Estimation

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Expressive Piano Performance Rendering

VirtuosoNet

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Score and Performance Alignment

Audio-to-Score Alignment, Score Following

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Pianist Motion Analysis and Generation

Performance Motion Analysis, Audio-to-Motion Generation, Performer Visualization


Singing Voice Analysis and Synthesis

Singing Voice Synthesis

Korean/English Singing Voice Synthesis, Multi-Singer Model

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Singing Voice Conversion

Singing Expression Transfer

Vocal Melody Extraction and Transcription in Polyphonic Music

Vocal Pitch Estimation and Note Transcription

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Singing Voice Tagging and Identification

Semantic Vocal Tagging, Embedding Learning

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LipSync Motion Generation


Symbolic Music Analysis and Generation

Symbolic Melody Similarity

Symbolic Music Representation, Symbolic Melody Retrieval

Symbolic Music Generation

Game BGM Generation, Drum Pattern Generation, Piano Music Generation

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Music Classificaiton and Retrieval

CNN Architectures for Music Classification

SampleCNN, Multi-level and Multi-scale Model

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Deep Audio Embedding Learning

Disentangled Representation Learning, Representation Learning with Meta Data

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Multimodal Music Retrieval

Musical Word Embedding, Zero-shot Learning for Music, Query-by-Image

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Intelligent Music Production

Sample Search, Sound Synthesis, and Digital Audio Effects

Drum Sample Retrieval, Automatic Parameter Estimation of Digital Audio Effects

AI DJ

DJ mix Analysis, Automatic Mix Generation, Beat-tracking, Music Structure Analysis

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Soundscape and AI

Neuroscape, Mixedscape


Korean Traditional Music

Expression Analysis

Haegum Expression Analysis and Notation, Gayagum Vibrato Analysis