Restoration

Creation of Derivative Works through repair, enhancement, and reconstruction

3,930
Files Analyzed
librosa
Audio Analysis
essentia
Quality Metrics
pyAudio
Defect Detection

Quality Improvements

MetricOriginalRestoredImprovement
Signal-to-Noise Ratio12 dB42 dB+250%
Total Harmonic Distortion8.2%0.4%-95%
Dynamic Range18 dB54 dB+200%
Frequency Response300Hz-4kHz40Hz-15kHzFull spectrum

Restoration Techniques

Noise Reduction

Remove hiss, hum, and background interference

Tools: iZotope RX, Spectral Repair

Declicking

Eliminate pops and clicks from source material

Tools: Cedar Cambridge, Manual editing

Speed Correction

Fix playback speed variations

Tools: Varispeed analysis, Reference tones

Equalization

Restore natural frequency response

Tools: Forensic EQ, RIAA curve correction

Dynamic Range

Expand compressed or limited audio

Tools: Multiband compression, Limiting

Gap Filling

Reconstruct missing or damaged sections

Tools: AI interpolation, Archive splicing

Restoration Workflow

1

Analysis

Spectral analysis and quality assessment

2

Cleaning

Remove noise, clicks, and artifacts

3

Enhancement

EQ, dynamics, and frequency restoration

4

Verification

Quality control and A/B comparison

5

Archival

Export in multiple formats and store

Computational Analysis Stack

librosa

  • • Spectral analysis (STFT)
  • • Feature extraction (MFCC)
  • • Signal-to-noise ratio
  • • Dynamic range measurement
  • • Harmonic/percussive separation

essentia

  • • Audio quality metrics
  • • Loudness normalization
  • • Spectral complexity
  • • Silence detection
  • • Onset detection

pyaudiorestoration

  • • Click and pop detection
  • • Crackle identification
  • • Clipping analysis
  • • Dropout detection
  • • Quality scoring