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Interactive 3D/Wavelet Transform
Wavelet Type
Haar: piecewise constant. DB2: smoother reconstruction.
Decomposition
Levels3
Denoising
Threshold0.50
Detail coeff |v| < threshold → set to 0
Energy per Subband
D1:
5.9%
D2:
14.1%
D3:
14.6%
A3:
65.4%
Key Insight
DWT decomposes a signal into frequency bands at multiple scales. D1 = finest detail (high freq), Dk = coarser. Thresholding small detail coefficients removes noise while preserving large features.

Wavelet Transform - Interactive Visualization

Wavelets provide time-frequency localization that Fourier transforms cannot: they can represent both low-frequency trends and high-frequency bursts precisely located in time. The Discrete Wavelet Transform (DWT) decomposes a signal into approximation (low-frequency) and detail (high-frequency) coefficients at multiple scales. Thresholding small detail coefficients removes noise while preserving structure.

  • See original signal decomposed into approximation and detail at 3 levels
  • Watch detail coefficients capture high-frequency features
  • Threshold small coefficients for denoising and see reconstructed signal
  • Compare energy in each sub-band
  • Foundation for image compression (JPEG 2000), ECG analysis, and signal denoising

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.