rex/dewitt
1
0
Fork 0
mirror of https://github.com/Luteva-ssh/dewitt synced 2026-01-13 06:11:36 +00:00
Code Issues Projects Releases Packages Wiki Activity
No description
5 commits 1 branch 0 tags 46 KiB
Find a file
Exact
Janni Adamski 79d0f6f9df ADDED: LICENCE - MIT
2025-07-07 02:26:56 +02:00
benchmarks First version that compiles and runs all tests/docs/benchmarks! :D 2025-07-07 02:04:18 +02:00
examples initial commit, first test 2025-07-07 01:04:14 +02:00
src First version that compiles and runs all tests/docs/benchmarks! :D 2025-07-07 02:04:18 +02:00
tests First version that compiles and runs all tests/docs/benchmarks! :D 2025-07-07 02:04:18 +02:00
.gitignore First version that compiles and runs all tests/docs/benchmarks! :D 2025-07-07 02:04:18 +02:00
config.nims initial commit, first test 2025-07-07 01:04:14 +02:00
dewitt.nimble initial commit, first test 2025-07-07 01:04:14 +02:00
LICENSE ADDED: LICENCE - MIT 2025-07-07 02:26:56 +02:00
README.md README changed. :) 2025-07-07 02:20:15 +02:00

README.md

Wavelet Audio Analysis

A comprehensive Nim implementation of Discrete Wavelet Transform (DWT) for audio signal processing and analysis.

Features

  • Complete DWT Implementation: Forward and inverse transforms with multiple wavelet families
  • Audio Processing: WAV file reading/writing, windowing, and preprocessing
  • Multiple Wavelets: Haar, Daubechies (db4, db8), Biorthogonal wavelets
  • Analysis Tools: Energy analysis, frequency decomposition, denoising
  • High Performance: Optimized algorithms with efficient memory usage
  • Comprehensive Tests: Unit tests and benchmarks included

Wavelet Families Supported

  • Haar: Simplest wavelet, good for signals with sharp transitions
  • Daubechies: Orthogonal wavelets with various orders (db4, db8)
  • Biorthogonal: Symmetric wavelets good for image/audio processing

Installation

nimble install

Usage

Basic DWT Example

import wavelet_audio

# Create a test signal
let signal = @[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]

# Perform DWT
let dwt = newDWT(WaveletType.Haar)
let (coeffs, lengths) = dwt.forward(signal)

# Reconstruct signal
let reconstructed = dwt.inverse(coeffs, lengths)

Audio Analysis Example

import wavelet_audio/audio

# Load audio file
let audio = loadWAV("input.wav")

# Analyze with DWT
let analyzer = newAudioAnalyzer(WaveletType.Daubechies4)
let analysis = analyzer.analyze(audio.samples, levels = 5)

# Get frequency band energies
echo "Energy distribution: ", analysis.energyDistribution

API Reference

Core DWT Classes

  • DWT: Main wavelet transform class
  • WaveletType: Enumeration of supported wavelets
  • AudioAnalyzer: High-level audio analysis interface

Key Methods

  • forward(signal): Perform forward DWT
  • inverse(coeffs, lengths): Perform inverse DWT
  • analyze(samples, levels): Analyze audio with multi-level DWT
  • denoise(signal, threshold): Remove noise using wavelet thresholding

Building and Testing

# Run tests
nimble test

# Build release version
nim c -d:release src/wavelet_audio.nim

# Generate documentation
nimble docs

Performance

The implementation is optimized for performance with:

  • In-place operations where possible
  • Efficient memory management
  • SIMD-friendly algorithms
  • Benchmark suite included

Applications

  • Audio denoising
  • Feature extraction for music analysis
  • Signal compression
  • Time-frequency analysis
  • Audio classification preprocessing

License

MIT License - see LICENSE file for details.

Contributing

Contributions welcome!