Ph.D. Candidate, Computer Science
(interactive) Machine Learning | Audio Signal Processing | HCI
Bongjun Kim is a Ph.D. candidate in the Department of Computer Science at Northwestern University and working at Interactive Audio Lab with Prof. Bryan Pardo. His current research interests include sound event detection, human-in-the-loop interfaces for audio annotation, interactive machine learning, and multimedia information retrieval. He also enjoys working on a musical interface and interactive media art.
7/15/2019: My paper, “Sound Event Detection Using Point-labeled Data” has been accepted from WASPAA 2019
7/01/2019: My DCASE submission (task 5) got 3rd place out of 22 systems competing. (2nd in team rankings). Read more about the challange and the results: click.
6/27/2019: I am giving a talk about “A Human-in-the-loop system for labeling sound events in audio recordings” at Midwest Music and Audio Day 2019 (MMAD) in Indiana Univesrity, Bloomington, USA.
6/03/2019: My paper, “Self-supervised Attention Model for Weakly Labeled Audio Event Classification” has been accepted from EUSIPCO 2019
5/11/2019: I am presenting my work, “Improving Content-based Audio Retrieval by Vocal Imitation Feedback” at ICASSP 2019 in Brighten, UK.
3/16/2019: I am giving a talk about my work, “A Human-in-the-loop System for Sound Event Detection and Annotation” at IUI 2019 in Los Angeles, USA.
11/19/2018: My sound classification model got 3rd place (out of 23 systems competing) in Making Sense of Sounds Data Challenge, 2018
11/19/2018: I am presenting my work, “Vocal Imitation Set: a dataset of vocally imitated sound events using the AudioSet ontology” at DCASE 2018 in Surrey, UK.