https://github.com/lucianodato/libspecbleach

C library for audio noise reduction and other spectral effects
https://github.com/lucianodato/libspecbleach

audio broadband-noise-reduction c fft library noise-reduction noise-removal non-stationary-noise-reduction spectral spectral-processing stationary-noise-reduction stft

Last synced: 11 days ago
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C library for audio noise reduction and other spectral effects

• Host: GitHub
• URL: https://github.com/lucianodato/libspecbleach
• Owner: lucianodato
• License: lgpl-2.1
• Created: 2022-04-02T16:43:54.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-20T04:22:56.000Z (almost 2 years ago)
• Last Synced: 2024-10-14T06:07:15.425Z (24 days ago)
• Topics: audio, broadband-noise-reduction, c, fft, library, noise-reduction, noise-removal, non-stationary-noise-reduction, spectral, spectral-processing, stationary-noise-reduction, stft
• Language: C
• Homepage:
• Size: 131 KB
• Stars: 66
• Watchers: 6
• Forks: 12
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# libspecbleach

C library for audio noise reduction and other spectral effects

[![build](https://github.com/lucianodato/libspecbleach/actions/workflows/build.yml/badge.svg)](https://github.com/lucianodato/libspecbleach/actions/workflows/build.yml)

## Background

This library is based on the algorithms that were used in [noise-repellent](https://github.com/lucianodato/noise-repellent). These were extracted into a this standalone library to remove the lv2 dependency. It was design to be extensible and modular. It uses the concept of a spectral processor which itself uses a short time Fourier transform (STFT) to process the audio. There are two initial processors in place, one which uses the adaptive part of noise repellent and one that uses the manual capturing profile based denoising. The library could be extended with more spectral processors using any STFT-based algorithm such as de-crackle, de-click and other audio restoration algorithms.

## De-noise algorithms

There several techniques implemented in the library that are being used in the denoisers, such as masking thresholds estimation, onset detectors, etc. All these are being used in conjunction to improve the very basic spectral substraction algorithm. Most of the papers used are listed in the wiki of the project. Also a block diagram is provided to explain the reduction architecture.

## Build

If you wish to compile yourself and install the library you will need the a C compiling toolchain, Meson build system, ninja compiler, git and fftw3 library.

Installation:

```bash

  git clone https://github.com/lucianodato/noise-repellent.git

  cd noise-repellent

  meson build --buildtype=release --prefix=/usr --libdir=lib (your-os-appropriate-location-fullpath)

  meson compile -C build -v

  sudo meson install -C build

```

## Example

Simple console apps examples are provided to demonstrate how to use the library. It needs libsndfile to compile successfully. You can use them as follows:

Adaptive noise learn

```bash

  adenoise_demo  

```

Manual noise learn

```bash

  denoise_demo  

```

It will recognize any libsndfile supported format.