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Wen-Chuan Chen

Education

National Tsing Hua University, Master of Science, Power Mechanical Engineering, 2017 ~ 2019

  • Division of Electrical Control, GPA: 4.17
  • Telecom Electro-Acoustic Audio Lab directed by Dr. Mingsian R. Bai
  • Master's Thesis: "Deep Learning Applied to Speech and Audio Signal Processing under Adverse Environments"

University@2x

National Sun Yat-sen university, Bachelor of Science, Mechanical and Electro-mechanical Engineering, 2013 ~ 2017

University@2x

Skills


Programming

  • Python
  • C / C++
  • Matlab
  • Github
  • OpenCV


Data Science

  • Machine / Deep Learning
  • Computer Vision
  • Music Information Retrieval


Computer Science

  • Object Oriented Programming
  • Data Structure and Algorithms
  • Probability and Statistic
  • Linear Algebra


Signal Processing

  • Audio / Image Signal Processing
  • Acoustic Array Signal Processing


Automatic Control

  • Digital Control System
  • probabilistic Robotics (Self-driving cars)

Language

  • Chinese - Native
  • English - TOEIC 920

Publication

Journal Paper: "Multichannel Sound Event Separation and Detection in Adverse Environments Using Convolutional Recurrent Neural Network with Complex Masking," The Journal of the Acoustical Society of America.

Propose an end-to-end polyphonic sound event separation and detection system aimed at separating and detecting multiple sound events. The system consists of a convolutional recurrent neural network in conjunction with an ideal complex mask. In addition, a discriminative training network is employed to increase the robustness.

Conference Paper: "Deep Learning Applied to Dereverberation and Sound Event Classification," International Congress on Acoustics, SEPT. 09-13, 2019 in Germany.

Investigates dereverberation and sound event detection techniques, with the aid of deep learning. The proposed system consists of two units: a multichannel deep neural front-end and a VGG-like classifier back-end trained with generic data augmented by various room impulse responses.

Conference Paper: "Robotic Voice Assistant Equipped with Binaural Audio," INTERNOISE, JUNE. 16-19, 2019 in Madrid.

The robot is comprised primarily of three functional units: (1) a microphone array used to locate the user position (2) a cloud-based DNN-based classifier for command words recognition (3) loudspeaker array for binaural audio sound effect.

Honors


  • Attended INTERNOISE conference, JUNE. 16-19, 2019 in Madrid
  • Graduate representative of College of Engineering, NTHU, 2019
  • Industry-academia Collaboration - Smart Acoustic Homecare System, 2017 - 2019
  • Director of Senior Project Contest - Command Word Recognition under Adverse Environments, 2018
  • Organized Chinese Society of Sound and Vibration 2018 at NTHU
  • Activities Director of Guitar club, NSYSU, 2014 - 2015
  • Vocalist Guitar club, NSYSU, 2013 - 2015
  • Director of Public Relations of MEM, NSYSU, 2013 - 2015

Side Projects

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Source Separation

Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process.

 My Implementation on Github

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Panorama Stitching 

Automatic panorama stitching technology has been widely adopted in many applications such as Google Street View, panorama photos on smartphones, and stitching software such as Photosynth and AutoStitch.

 My Implementation on Github