Intelligent Assistive Listening Devices and Acoustic Laboratory
Research Field
Dr. Hung-Yue Chang is an Associate Professor in the Department of Mechanical Engineering at National Chung Hsing University, Taiwan. He currently serves as a reviewer for the International Journal of Advanced Manufacturing Technology. His achievements have been recognized through several honors, including the Merry Electroacoustic Thesis Award, the Distinguished Teaching Award and the Favorite Teacher Award of National Chung Hsing University.
Dr. Chang has been actively involved in the Chinese Society of Sound and Vibration, serving as a paper reviewer and session chair for numerous academic conferences. Over the years, he has devoted his research efforts to electroacoustic transducers and the application of microphone array technologies in hearing-assistive devices and intelligent manufacturing systems. He has served as the principal investigator of multiple projects funded by the National Science and Technology Council (NSTC), led numerous industry–academia collaborative projects, and participated in the Advanced Intelligent Manufacturing Technology Alliance.
During the past five years, Dr. Chang has published four SCI-indexed journal papers, including one paper in a JCR Q1 journal, and has been granted five invention patents. He has also supervised students who have received several Best Student Paper Awards at academic conferences. His recent research has focused on microphone array applications in hearing-assistive technologies and intelligent manufacturing systems, resulting in publications in prestigious journals such as IEEE Sensors Journal and the International Journal of Advanced Manufacturing Technology.
In addition, Dr. Chang has established a long-term industry–academia partnership with Merry Electronics Co., Ltd. He possesses extensive experience in industrial collaboration, technology transfer, and product development, serving as a technical consultant for the company and successfully completing multiple patent licensing and technology transfer projects.
The main focus of the Intelligent Assistive Listening Devices and Acoustic Lab is to develop intelligent hearing devices using AI technology and to apply acoustic sensors—especially array technology—in smart manufacturing. The lab's core research areas include the following:
- Directional Microphone and Speaker Arrays.
- Application of Acoustic and Vibration Sensors in Tool Wear Monitoring Systems.
- Hearing Aid Self-fitting Strategy.
Research Topic
Development of Frequency-Domain Beamforming Algorithms for Acoustic Camera-Based Tool Wear Monitoring
Background
Acoustic cameras have emerged as a promising non-contact sensing technology for monitoring machining processes in smart manufacturing environments. Compared with conventional vibration sensors, microphone arrays can provide both sound source localization and acoustic feature extraction. However, in enclosed machine tools, machining sounds are often contaminated by reflections and background noise, limiting the effectiveness of conventional beamforming methods. Advanced frequency-domain beamforming algorithms, such as Multiple Signal Classification (MUSIC), have the potential to improve spatial resolution and sound source localization accuracy for tool condition monitoring.
Objectives
The objective of this internship project is to develop and evaluate frequency-domain beamforming algorithms for acoustic-camera-based tool wear monitoring. The intern will implement and compare advanced beamforming methods, including MUSIC, for sound source localization and acoustic imaging in machining environments.
Research Tasks
1.Study the principles of microphone array signal processing and frequency-domain beamforming.
2. Develop frequency-domain beamforming algorithms, including:
- Multiple Signal Classification (MUSIC)
- Other high-resolution beamforming algorithms (optional)
3. Implement the algorithms using the UMA16 microphone array platform.
4. Generate acoustic images of simulated drilling sound sources inside an enclosed machine tool environment under electric spindle noise conditions.
5. Evaluate localization accuracy, spatial resolution, and noise robustness of the developed algorithms.
6. Compare the self-developed acoustic imaging results with those obtained from a commercial Gfai Ring32 acoustic camera system.
Experimental Platform
- Microphone Array: UMA16 USB Microphone Array (16-channel MEMS microphone array)
- Software Environment: Python
- Reference System: Gfai Ring32 AC Pro Acoustic Camera (available in our lab)
- Target Application: Tool wear monitoring in drilling operations
Expected Outcomes
By the end of the internship, the student is expected to:
- Develop a complete frequency-domain beamforming framework for acoustic imaging.
- Implement and validate MUSIC-based sound source localization.
- Quantitatively compare localization performance with a commercial acoustic camera system.
- Generate technical documentation and experimental results suitable for publication or future research projects in smart manufacturing and acoustic sensing.
Internship Period
December 2026 – February 2027
2018 Merry Electroacoustic Thesis Award (Ph.D Dissertation)
2024 Distinguished Teaching Award
2024, 2025 Favorite Teacher Award
- Ph.D. National Cheng-Kung University, Taiwan(2010-09~2018-01)
- MS Pennsylvania State University, USA (1994-08~1996-05)
- BS National Cheng-Kung University, Taiwan(1987-09~1991-06)
Job Description
N/A
Preferred Intern Educational Level
Ph.D. student
Skill sets or Qualities
Essential Qualifications
- Ph.D. student in Mechanical Engineering, Acoustics, Mechatronics, or a related field.
- Basic knowledge of digital signal processing (DSP), including Fourier Transform (FFT) and frequency-domain analysis.
- Experience with programming in MATLAB and/or Python.
- Familiarity with data analysis and scientific computing.
- Good communication skills and the ability to work independently in a research environment.
Preferred Qualifications
- Knowledge of microphone arrays and beamforming techniques.
- Experience with acoustic signal processing or sound source localization.
- Familiarity with linear algebra and matrix operations.
- Experience implementing signal processing algorithms such as:
- Delay-and-Sum (DAS) beamforming
- Minimum Variance Distortionless Response (MVDR)
- Multiple Signal Classification (MUSIC)
- Experience with machine learning or pattern recognition.
- Experience using MATLAB Phased Array System Toolbox or equivalent signal processing libraries.
- Familiarity with acoustic imaging systems and smart manufacturing applications.
- Familiarity with machine tool operation.
Desired Personal Attributes
- Strong analytical and problem-solving skills.
- Interest in acoustics, smart manufacturing, and industrial sensing technologies.
- Self-motivated and willing to learn new signal processing techniques.
- Ability to document research results and prepare technical reports.