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time estimation


Proposed blind reverberation time estimation using deep neural networks (DNN) 

using multi-channel microphone

Proposed both Single channel & multi-channel methods

- Obtaining the acoustic parameter from the sound source

- Conducted an intensive study with single & multiple channel based algorithm using neural networks.  

  (Published in IEEE and Acta Acustica)
- Developed estimation algorithm for dereverberation and an acoustic model (C, MATLAB)
- Contributed distributive research with LG electronics

= Publication

◇ 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.


= Patent

- Multichannel Microphone-based Reverberation Time Estimation Method and Device which use Deep Neural Network Technical Field, WO/US Patent, 2017.
- Multi-Channel Microphone based Reverberation Time Estimation using Deep Neural Network, Korea Patent, 2016.


Performance of T60 estimation algorithms 

in various noise environments for different SNRs:

(a) bias (b) MSE, and (c) ρ.

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