FFT Signal Processing Simulator

Exploring Geometric Constraints in Frequency Domain

Fourier's Theorem: Any periodic signal can be decomposed into a sum of sine waves.

Constraint Theory: The frequency domain representation is a set of geometric constraints that shape the signal.

Signal Controls

Harmonics

Filter

Display Options

Time Domain - Signal Waveform

Draw on canvas to create custom signal

Frequency Domain - FFT Spectrum

Drag to set filter cutoff frequency

Individual Harmonics

Phase Spectrum

Reconstructed Signal (Inverse FFT)

Signal Statistics

RMS Value: 0.000
Peak Amplitude: 0.000
Dominant Frequency: 0 Hz
Bandwidth: 0 Hz
THD: 0%
SNR: 0 dB

Constraint Theory Perspective

Geometric Constraints in Frequency Domain

The FFT reveals that any signal is constrained by its frequency components. Each frequency acts as a geometric constraint that shapes the signal's behavior in time.

Key Insights:

  • Harmonic Series: Integer multiples of fundamental frequency create geometric progressions in frequency space.
  • Pythagorean Ratios: Musical intervals emerge naturally from frequency ratios (2:1 octave, 3:2 fifth, 4:3 fourth).
  • Constraint Propagation: Removing frequency components (filtering) propagates constraints through the signal, altering its shape.
  • Time-Frequency Duality: Sharp features in time require broad frequency support (uncertainty principle).

Interactive Exploration:

  • Draw custom signals to see their frequency content
  • Add harmonics to understand complex waveform construction
  • Apply filters to see constraint propagation
  • Watch animated decomposition to see harmonics building the signal