Taejun Kim

MIR Researcher, Software/ML Engineer

About Me

When I was an undergraduate, I took semesters off to be a guitarist but had to program for a living. However, I realized I enjoy programming and quite good at it so I came back to school. Since then I have improved my software and data engineering skills but my passion for music was aroused again so I joined Music and Audio Computing Lab dreaming of making AI DJ. The first research I wanted to do for AI DJ was making a machine listening system, therefore I’ve worked on end‐to‐end audio classification.

I also worked as a resident DJ at a dance club called Vent until before COVID‐19 and still enjoy playing music at events. With the domain knowledge, I work on understanding the creative process of DJing using computational methods and creating AI DJ.

I’ve been wondering how people see beauty in music, and somehow, I currently work on building machines that can understand the beauty and assist music for humans.

Web Demonstrations

DJ StructFreak 🤖

AI DJ Demo

Music Dissector 🔪

Interactive Music Structure Visualizer

DJ Mixer Analyzer 🎛️

Explains How DJ Controlled Mixer


All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio

Taejun Kim, and Juhan Nam

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2023

Temporal Feedback Convolutional Recurrent Neural Networks for Speech Command Recognition

Taejun Kim, and Juhan Nam

The Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2022

Joint Estimation of Fader and Equalizer Gains of DJ Mixers using Convex Optimization

Taejun Kim, Yi-Hsuan Yang, and Juhan Nam

The International Conference on Digital Audio Effects (DAFx), 2022

Reverse-Engineering The Transition Regions of Real-World DJ Mixes using Sub-band Analysis with Convex Optimization

Taejun Kim, Yi-Hsuan Yang, and Juhan Nam

The International Conference on New Interfaces for Musical Expression (NIME), 2021

A Computational Analysis of Real-World DJ Mixes using Mix-To-Track Subsequence Alignment

Taejun Kim, Minsuk Choi, Evan Sacks, Yi-Hsuan Yang, and Juhan Nam

The International Society for Music Information Retrieval Conference (ISMIR), 2020

Comparison and Analysis of SampleCNN Architectures for Audio Classification

Taejun Kim, Jongpil Lee, and Juhan Nam

IEEE Journal of Selected Topics in Signal Processing, 2019

Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms

Taejun Kim, Jongpil Lee, and Juhan Nam

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

Raw Waveform-based Audio Classification Using Sample-level CNN Architectures

Jongpil Lee, Taejun Kim, Jiyoung Park, and Juhan Nam

Machine Learning for Audio Signal Processing Workshop, Neural Information Processing Systems (NIPS), 2017


Korea Advanced Institute of Science and Technology (KAIST)

Ph.D Candidate in Graduate School of Culture Technology

2019 - present

@ Music and Audio Computing Lab (Advisor: Juhan Nam)

Dissertation Title (tentative): A Computational Approach to the Analysis of Electronic Dance Music and DJ Mixes

Proposal Slides: [keynote] [pdf]

University of Seoul

M.S. in Electrical and Computer Engineering

2017 - 2019

@ Data Mining Lab (Advisor: Hanjoon Kim)

University of Seoul

B.S. in Electrical and Computer Engineering

2012 - 2017




2020 - present

Research Interests

AI DJ, Music Information Retrieval, Machine Learning, Signal Processing, Musical Applications


Machine Learning

PyTorch, Scikit‐learn, Weights & Biases, TensorFlow

Data Engineering

Crawling, Elasticsearch, MongoDB, AWS Batch, Apache Spark

Data Analysis

Pandas, Numpy, Matplotlib, SQL, Jupyter Lab, Apache Zeppelin

Signal Processing

Librosa, Madmom, SciPy

Cloud Engineering

(Experienced in AWS) AWS Batch, Step Functions, Lambda, Cognito, S3, Elastic Beanstalk, API Gateway, Cloudfront, etc.

Web Development

Vue.js, Flask, Node.js, AWS Amplify


Python, JavaScript, Scala, SQL, R, Java

Musical Performance

Vinyl & Digital DJ, Electric Bass Guitar, Acoustic Fingerstyle Guitar