Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2026 356009 Microphone array signal processing 1 3.0 3 ★ KUNG,FAN-JIE GEE
IEECSG 13 0 Jointly offered by Electrical Engineering department.

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	356009	Microphone array signal processing	1	3.0	3	★	KUNG,FAN-JIE	GEE IEECSG	13	0	Jointly offered by Electrical Engineering department.

Course Plan：

Instructor KUNG,FAN-JIE　Office Hours

Email fjkung@ntut.edu.tw

Last Updated 2025-12-19 13:42:07

Course Objective 培養學生使用多通道波束形成器技術應用在降噪、降混響，並使用Matlab撰寫多通道語音增強演算法。學生可以學習到: 1. 空間取樣概念。2. 延遲相加波束形成器技術。3. 最小變異無失真響應波束形成器技術。4. 線性限制最小變異波束形成器技術。5. 廣義旁辦消除波束形成器技術。6. 多通道維也納濾波器降噪降混響技術。7. 線性卡爾曼濾波器結和廣義旁辦消除波束形成器技術。 Students will learn to use multichannel beamformer techniques and MATLAB for speech enhancement. Students will learn: 1. The Spatial sampling concept. 2. Delay-and-sum (DAS) beamforming technique. 3. Minimum variance distortionless response (MVDR) beamforming technique. 4. Linearly constrained minimum variance (LCMV) beamforming technique. 5. Generalized sidelobe canceller (GSC) beamforming technique. 6. Multichannel Wiener filtering for denoising and dereverberation. 7. Integrated sidelobe cancellation and linear prediction Kalman filter (ISCLP)

Course Schedule Week 1: Course introduction Weeks. 2-4: Review of STFT and classical optimal filtering (Homework 1) Weeks. 5-8: Spatial filtering introduction: DAS, LCMP, LCMV, GSC, MPDR, MVDR algorithms (Homework 2) Weeks. 9-12: Multichannel Wiener filtering (MWF), BMWF, ISCLP algorithms introduction (Homework 3) Weeks. 13-16: Subspace-based methods for source counting (Homework 4) Weeks. 17-18: Final presentation

Evaluation and grading policy Quiz: 30%, Homework: 30%, Term project: 30%, Participation: 10%, Bonus

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
References- 1. J. Benesty, J. Chen, and Y. Huang. Microphone Array Signal Processing, Springer 2. M. R. Bai, J.-G. Ih, and J. Benesty. Acoustic Array Systems: Theory, Implementation, and Application, Wiley 3. I. Kodrasi and S. Doclo, "Joint late reverberation and noise power spectral density estimation in a spatially homogeneous noise field," ICASSP, 2018. 4. T. Dietzen, S. Doclo, M. Moonen, and T. van. Waterschoot, "Integrated sidelobe cancellation and linear prediction Kalman filter for joint multi-microphone speech dereverberation, interfering speech cancellation, and noise reduction," IEEE Transactions on Audio, Speech, and Language Processing, 2020. 5. F.-J. Kung, Microphone array signal processing for speaker counting, speech enhancement, and source separation in adverse environments, Ph.D. dissertation, National Tsing Hua University, Taiwan, Jan. 2024

The access to curricular consultation Email: fjkung@ntut.edu.tw
Office hours: Tuesday (9:00-10:00), Thursday (9:00-12:00)
Office: 綜合科館518

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG17：夥伴關係（Partnerships for the Goals）

Does the course incorporate AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

Note
　

Instructor	KUNG,FAN-JIE　Office Hours
Email	fjkung@ntut.edu.tw
Last Updated	2025-12-19 13:42:07
Course Objective	培養學生使用多通道波束形成器技術應用在降噪、降混響，並使用Matlab撰寫多通道語音增強演算法。學生可以學習到: 1. 空間取樣概念。2. 延遲相加波束形成器技術。3. 最小變異無失真響應波束形成器技術。4. 線性限制最小變異波束形成器技術。5. 廣義旁辦消除波束形成器技術。6. 多通道維也納濾波器降噪降混響技術。7. 線性卡爾曼濾波器結和廣義旁辦消除波束形成器技術。 Students will learn to use multichannel beamformer techniques and MATLAB for speech enhancement. Students will learn: 1. The Spatial sampling concept. 2. Delay-and-sum (DAS) beamforming technique. 3. Minimum variance distortionless response (MVDR) beamforming technique. 4. Linearly constrained minimum variance (LCMV) beamforming technique. 5. Generalized sidelobe canceller (GSC) beamforming technique. 6. Multichannel Wiener filtering for denoising and dereverberation. 7. Integrated sidelobe cancellation and linear prediction Kalman filter (ISCLP)
Course Schedule	Week 1: Course introduction Weeks. 2-4: Review of STFT and classical optimal filtering (Homework 1) Weeks. 5-8: Spatial filtering introduction: DAS, LCMP, LCMV, GSC, MPDR, MVDR algorithms (Homework 2) Weeks. 9-12: Multichannel Wiener filtering (MWF), BMWF, ISCLP algorithms introduction (Homework 3) Weeks. 13-16: Subspace-based methods for source counting (Homework 4) Weeks. 17-18: Final presentation
Evaluation and grading policy	Quiz: 30%, Homework: 30%, Term project: 30%, Participation: 10%, Bonus
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes References- 1. J. Benesty, J. Chen, and Y. Huang. Microphone Array Signal Processing, Springer 2. M. R. Bai, J.-G. Ih, and J. Benesty. Acoustic Array Systems: Theory, Implementation, and Application, Wiley 3. I. Kodrasi and S. Doclo, "Joint late reverberation and noise power spectral density estimation in a spatially homogeneous noise field," ICASSP, 2018. 4. T. Dietzen, S. Doclo, M. Moonen, and T. van. Waterschoot, "Integrated sidelobe cancellation and linear prediction Kalman filter for joint multi-microphone speech dereverberation, interfering speech cancellation, and noise reduction," IEEE Transactions on Audio, Speech, and Language Processing, 2020. 5. F.-J. Kung, Microphone array signal processing for speaker counting, speech enhancement, and source separation in adverse environments, Ph.D. dissertation, National Tsing Hua University, Taiwan, Jan. 2024
The access to curricular consultation	Email: fjkung@ntut.edu.tw Office hours: Tuesday (9:00-10:00), Thursday (9:00-12:00) Office: 綜合科館518
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG17：夥伴關係（Partnerships for the Goals）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
Note