Machine learning-based algorithm development
- Acoustic Parameter Estimation
Acoustic gesture recognition
- Gesture Recognition
Gestural recognition using smartphones
Link available for individual projects
Developed blind reverberation time estimation using deep neural networks (DNN) using a multi-channel microphone
- Obtaining the acoustic parameter from the sound source
◇ Myungin Lee, Joon-Hyuk Chang, "Deep neural network based blind estimation of reverberation time based on multi-channel microphones," Acta Acustica united with Acustica, 2018.
◇ Myungin Lee, Joon-Hyuk Chang, “Blind Estimation of Reverberation Time on Multi-Channel Microphone using Deep Neural Network,” Master’s thesis, Feb., 2017.
◇ Myungin Lee, Joon-Hyuk Chang, “Blind Estimation of Reverberation Time using Deep Neural Network,” IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC), Beijing, China, Sep., 2016.
◇ Jeehye Lee, Myungin Lee, Joon-Hyuk Chang, “Ensemble of Jointly Trained Deep Neural Network-Based Acoustic Models for Reverberant Speech Recognition,” arXiv:1608.04983, 2016.
Developed and performed machine learning & signal processing based interactive-musical system
◇ Myungin Lee, "Deep neural network based music source conducting system," International Computer Music Conference (ICMC), 2018.
- Developed general-purpose gesture-based smartphone 3D interface (C++).
- Developing a multimodal 3D interface with pattern recognition using signal processing & machine learning.
◇ Myungin Lee, “Entangled: A Multi-Modal, Multi-User Interactive Instrument in Virtual 3D Space Using the Smartphone for Gesture Control," New Interfaces for Musical Expression (NIME'21), Jun., 2021.
◇ Myungin Lee, “A Multi-User Interactive Instrument in the 3D Space Using the Gesture of Smartphones,” Korea Electro-Acoustic Music Society's Annual Conference (KEAMSAC), Oct., 2019.