Fourier Transform & Noise Suppression Research

Project Overview

This comprehensive research project explores the practical application of Discrete Fourier Transform (DFT) in digital noise suppression algorithms used by modern video conferencing platforms. Conducted under the mentorship of Arthur Western, Professor Emeritus of Physics and Optical Engineering at Rose-Hulman Institute of Technology, this research investigates how applications like Zoom identify and suppress ambient noise while preserving speech clarity through the unique lens of analyzing Rubik's Cube turning sounds. The 15-page paper, titled "Application of the Fourier Transform to Explain Noise Suppression in Relation to the Rubik's Cube," combines theoretical signal processing concepts with experimental analysis to understand real-world noise suppression technologies.

Key Research Achievements

Technical Stack & Methodology

The research employed MATLAB as the primary tool for signal processing and analysis. The methodology involved recording Rubik's Cube turning sounds under controlled conditions, then analyzing these audio signals using DFT to understand their frequency characteristics. The study compared the original audio signals with their processed versions after passing through Zoom's noise suppression algorithm, identifying the specific frequency ranges and thresholds where suppression occurs. Mathematical analysis was combined with practical experimentation to validate theoretical predictions about noise suppression behavior.

Research Challenges & Methodology

One of the primary challenges was designing experiments that could accurately capture and analyze the subtle differences in audio processing by proprietary algorithms. I addressed this by developing a systematic approach using controlled audio recording environments and consistent experimental conditions. Another challenge was interpreting the complex frequency domain data and correlating it with real-world noise suppression behavior. This required deep understanding of both theoretical signal processing concepts and practical audio engineering principles, bridging the gap between academic theory and industrial application.

Research Findings & Impact

The research successfully identified the frequency threshold at which Zoom's digital noise suppression algorithm operates, providing insights into how modern communication platforms balance noise reduction with speech preservation. The findings demonstrate the practical application of Fourier Transform theory in everyday technology, showing how mathematical concepts learned in academic settings translate to real-world engineering solutions. This research contributes to the understanding of digital signal processing in communication systems and provides a foundation for further investigation into audio processing algorithms. The work showcases the intersection of theoretical mathematics, experimental methodology, and practical engineering applications.

Research Paper