Principles of Spectrum Analysis
The core principle of spectrum analysis is based on the understanding that any complex, time-varying signal can be represented as a sum of simple sinusoidal waves (sine and cosine functions) of various frequencies, amplitudes, and phases. This representation is made possible through the use of mathematical transformations that convert time-domain signals (signals represented as variations over time) into frequency-domain signals (signals represented as variations over frequency).
This transformation is crucial for analyzing signals whose characteristics are more readily understood in the frequency domain.
For example, identifying the dominant frequencies in a piece of music or detecting the frequency of a periodic signal corrupted by noise.
Spectrum Analysis in Python
Spectrum analysis is a powerful technique used in signal processing to analyze the frequency content of signals. It finds applications in various fields such as telecommunications, audio processing, and vibration analysis. In this article, we’ll explore the fundamentals of spectrum analysis and how it can be implemented in Python.
Table of Content
- What is Spectrum Analysis?
- Types of Spectrum Analysis
- Principles of Spectrum Analysis
- How to do Spectrum Analysis?
- 1. Fast Fourier Transform (FFT)
- 2. Power Spectral Density (PSD)
- Applications of Spectrum Analysis