About me



Research Interests

  • Music Information Retrieval
  • Deep Learning


Career

  • 2020 - now. CEO (Co-founder), Neutune, Seoul, South Korea


Education

  • 2017 - 2021. PhD (Thesis), Graduate School of Culture Technology, KAIST, Daejeon, South Korea (Advisor: Juhan Nam)
  • 2015 - 2017. MS, Graduate School of Culture Technology, KAIST, Daejeon, South Korea
  • 2007 - 2015. BS, EE, Hanyang University, Seoul, South Korea


Experience

  • 2020. Visiting PhD, Music and Audio Research Laboratory, New York University, New York City, United States
  • 2019. Research Intern, Audio Research Group, Adobe, San Francisco, United States
  • 2017. Research Intern, Clova Artificial Intelligence Research, Naver, Seongnam, South Korea


Tutorial

Metric Learning for Music Information Retrieval

Brian McFee, Jongpil Lee, and Juhan Nam
International Society of Music Information Retrieval Conference (ISMIR), 2020

Waveform-based Music Processing with Deep Learning

Jordi Pons, Jongpil Lee, and Sander Dieleman
International Society of Music Information Retrieval Conference (ISMIR), 2019


Journals

Semantic Tagging of Singing Voices in Popular Music Recordings

Keunhyoung Luke Kim, Jongpil Lee, Sangeun Kum, Chae Lin Park, and Juhan Nam
IEEE/ACM Transactions on Audio, Speech and Language Processing, 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

Deep Learning for Audio-based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach

Juhan Nam, Keunwoo Choi, Jongpil Lee, Szu-Yu Chou, and Yi-Hsuan Yang
IEEE Signal Processing Magazine, 2019

SampleCNN: End-to-End Deep Convolutional Neural Networks Using Very Small Filters for Music Classification

Jongpil Lee, Jiyoung Park, Keunhyoung Luke Kim, and Juhan Nam
Applied Sciences, 2018

Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging

Jongpil Lee, and Juhan Nam
IEEE Signal Processing Letters, 2017


Exhibition

NEUROSCAPE: Artificial Soundscape Based on Multimodal Connections of Deep Neural Networks

Seungsoon Park, Jongpil Lee, and Juhan Nam
International Computer Music Conference (ICMC), 2018


Conferences, Workshops, Challenges, Demos

Learning a Cross-domain Embedding Space of Vocal and Mixed Audio with a Structure-preserving Triplet Loss

Keunhyoung Luke Kim, Jongpil Lee, Sangeun Kum, and Juhan Nam
International Society of Music Information Retrieval Conference (ISMIR), 2021

Mixed Scape: Development of Framework and Artwork for Auditory Correspondence in Mixed Reality

Seungsoon Park, Jongpil Lee, Taewan Kim, Tae Hong Park, Joonhyung Bae, and Juhan Nam
International Computer Music Conference (ICMC), 2021

Metric Learning VS Classification for Disentangled Music Representation Learning

Jongpil Lee, Nicholas J. Bryan, Justin Salamon, Zeyu Jin, and Juhan Nam
International Society of Music Information Retrieval Conference (ISMIR), 2020

Musical Word Embedding: Bridging the Gap between Listening Contexts and Music

Seungheon Doh, Jongpil Lee, Tae Hong Park, and Juhan Nam
Machine Learning for Media Discovery Workshop, International Conference on Machine Learning (ICML), 2020 (accepted)

Disentangled Multidimensional Metric Learning for Music Similarity

Jongpil Lee, Nicholas J. Bryan, Justin Salamon, Zeyu Jin, and Juhan Nam
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
*** IEEE SPS Student Travel Grant ***

Zero-shot Learning for Audio-based Music Classification and Tagging

Jeong Choi*, Jongpil Lee*, Jiyoung Park, and Juhan Nam
International Society of Music Information Retrieval Conference (ISMIR), 2019
* Equally contributed

A Cross-Scape Plot Representation for Visualizing Symbolic Melodic Similarity

Saebyul Park, Taegyun Kwon, Jongpil Lee, Jeounghoon Kim, and Juhan Nam
International Society of Music Information Retrieval Conference (ISMIR), 2019

Representation Learning of Music Using Artist, Album, and Track Information

Jongpil Lee, Jiyoung Park, and Juhan Nam
Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML), 2019

Zero-shot Learning and Knowledge Transfer in Music Classification and Tagging

Jeong Choi, Jongpil Lee, Jiyoung Park, and Juhan Nam
Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML), 2019

Deep Content-User Embedding Model for Music Recommendation

Jongpil Lee, Kyungyun Lee, Jiyoung Park, Jangyeon Park, and Juhan Nam
https://arxiv.org/abs/1807.06786, 2018

Representation Learning of Music Using Artist Labels

Jiyoung Park*, Jongpil Lee*, Jangyeon Park, Jung-Woo Ha, and Juhan Nam
International Society of Music Information Retrieval Conference (ISMIR), 2018
* Equally contributed

A Hybrid of Deep Audio Feature and i-vector for Artist Recognition

Jiyoung Park, Donghyun Kim, Jongpil Lee, Sangeun Kum, and Juhan Nam
Joint Workshop on Machine Learning for Music, International Conference on Machine Learning (ICML), 2018

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

Building K-POP Singing Voice Tag Dataset: A Progress Report

KeunHyoung Luke Kim, Sangeun Kum, Chae Lin Park, Jongpil Lee, Jiyoung Park, and Juhan Nam
Late-Breaking/Demo Session of International Society of Music Information Retrieval Conference (ISMIR), 2017

MUSIC GALAXY HITCHHIKER: 3D Web Music Navigation Through Audio Space Trained with Tag and Artist Labels

Dongwoo Suh, Kyungyun Lee, Jongpil Lee, Jiyoung Park, and Juhan Nam
Late-Breaking/Demo Session of International Society of Music Information Retrieval Conference (ISMIR), 2017

Cross-cultural Transfer Learning Using Sample-level Deep Convolutional Neural Networks

Jongpil Lee, Jiyoung Park, Chanju Kim, Adrian Kim, Jangyeon Park, Jung-Woo Ha, and Juhan Nam
Music Information Retrieval Evaluation eXchange (MIREX), 2017
1st place in the four K-POP tasks across all algorithms submitted so far

Representation Learning Using Artist labels for Audio Classification Tasks

Jiyoung Park, Jongpil Lee, Jangyeon Park, Jung-Woo Ha, and Juhan Nam
Music Information Retrieval Evaluation eXchange (MIREX), 2017
1st place in the Music Mood Classification task across all algorithms submitted so far

Combining Multi-Scale Features Using Sample-level Deep Convolutional Neural Networks for Weakly Supervised Sound Event Detection

Jongpil Lee, Jiyoung Park, Sangeun Kum, Youngho Jeong, and Juhan Nam
Proceedings of the 2nd Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2017

Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification

Jongpil Lee, and Juhan Nam
Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML), 2017

Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms

Jongpil Lee, Jiyoung Park, Keunhyoung Luke Kim, and Juhan Nam
Sound and Music Computing Confenrence (SMC), 2017

The Effect of DJs’ Social Network on Music Popularity

Hyeongseok Wi, Kyung hoon Hyun, Jongpil Lee, and Wonjae Lee
International Computer Music Conference (ICMC), 2016


Contact

  • jongpillee.brian (at) gmail.com
  • richter (at) kaist.ac.kr