Signal Processing (COMP6987001)
Undergraduate course, BINUS University, Artificial Intelligence Program, 2026
A 2-credit course introducing the fundamental concepts and practical techniques of digital signal processing using Python: acquiring, visualizing, filtering, analyzing, and interpreting signals in the time and frequency domains, with applications in audio processing, biomedical signals, IoT sensor analytics, and intelligent systems.
Sessions
- Sessions 1-3: Signal Fundamentals — introduction to signal processing, data acquisition and signal input, signal visualization
- Sessions 4-6: Filtering and Features — digital signal filtering, practical signal filtering, event and feature detection
- Sessions 7-8: Statistics and Modeling — statistical signal analysis, parameter fitting and signal modeling
- Sessions 9-10: Frequency Domain — spectral signal analysis, time-frequency signal analysis
- Sessions 11-13: Machine Learning and Applications — machine learning for signal processing, signal processing for AI and intelligent systems, review and presentation
Learning Outcomes
- Explain the fundamental concepts of digital signal processing
- Implement signal processing techniques using Python
- Apply statistical, spectral, and machine learning methods to signal problems
- Evaluate signal processing workflows for intelligent systems, IoT, and AI applications
Resources
- Textbook: Haslwanter (2023). Hands-On Signal Analysis with Python (Springer)
- Tools: NumPy, SciPy, pandas, Matplotlib, librosa
Full session materials are shared with enrolled students only.