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https://github.com/libAudioFlux/audioFlux
A library for audio and music analysis, feature extraction.
https://github.com/libAudioFlux/audioFlux
audio audio-analysis audio-features audio-processing deep-learning machine-learning mfcc mir music music-analysis music-information-retrieval pitch python signal-processing spectral-analysis spectrogram time-frequency-analysis wavelet-analysis wavelet-transform
Last synced: about 2 months ago
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A library for audio and music analysis, feature extraction.
- Host: GitHub
- URL: https://github.com/libAudioFlux/audioFlux
- Owner: libAudioFlux
- License: mit
- Created: 2023-01-16T09:53:04.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-05-22T14:07:27.000Z (7 months ago)
- Last Synced: 2024-05-22T15:31:58.390Z (7 months ago)
- Topics: audio, audio-analysis, audio-features, audio-processing, deep-learning, machine-learning, mfcc, mir, music, music-analysis, music-information-retrieval, pitch, python, signal-processing, spectral-analysis, spectrogram, time-frequency-analysis, wavelet-analysis, wavelet-transform
- Language: C
- Homepage: https://audioflux.top
- Size: 7.1 MB
- Stars: 2,070
- Watchers: 28
- Forks: 100
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-python-audio - audioFlux
- project-awesome - libAudioFlux/audioFlux - A library for audio and music analysis, feature extraction. (C)
- awesome-python-zh - audioFlux - 用于音频和音乐分析，特征提取的库。 (音频)
- awesome-music - audioFlux - A library for audio and music analysis, feature extraction. (Audio Libraries)
- awesome-musicdsp - audioflux - AudioFlux is a new-kid-on the block and some what similar but looks designed for deep learning, it looks very promising and has a liberal (MIT) license. (Music Information Retrieval Libraries (C/C++))
- awesome-python - audioFlux - A library for audio and music analysis, feature extraction. (Audio)
- awesome-hacking-lists - libAudioFlux/audioFlux - A library for audio and music analysis, feature extraction. (C)
- awesome-music - audioFlux - A library for audio and music analysis, feature extraction. (Audio Libraries)
README
# audioFlux
[](https://pypi.org/project/audioflux/)
[](https://pypi.org/project/audioflux/)
[](https://audioflux.top/index.html)
[](https://doi.org/10.5281/zenodo.7548288)
**`audioflux`** is a deep learning tool library for audio and music analysis, feature extraction. It supports dozens of
time-frequency analysis transformation methods and hundreds of corresponding time-domain and frequency-domain feature
combinations. It can be provided to deep learning networks for training, and is used to study various tasks in the audio
field such as Classification, Separation, Music Information Retrieval(MIR) and ASR etc.
##### New Features
* v0.1.8
* Add a variety of Pitch algorithms: `YIN`, `CEP`, `PEF`, `NCF`, `HPS`, `LHS`, `STFT` and `FFP`.
* Add `PitchShift` and `TimeStretch` algorithms.
### Table of Contents
- [Overview](#overview)
- [Installation](#installation)
- [Python Package Install](#python-package-install)
- [Other Build](#other-build)
- [Quickstart](#quickstart)
- [Benchmark](#benchmark)
- [Documentation](#documentation)
- [Contributing](#contributing)
- [Citing](#citing)
- [License](#license)
## Overview
**`audioFlux`** is based on data stream design. It decouples each algorithm module in structure, and can quickly and
efficiently extract features of multiple dimensions. The following is the main feature architecture diagram.
You can use multiple dimensional feature combinations, select different deep learning networks training, study various
tasks in the audio field such as Classification, Separation, MIR etc.
The main functions of **`audioFlux`** include **transform**, **feature** and **mir** modules.
#### 1. Transform
In the time–frequency representation, main transform algorithm:
- **`BFT`** - Based Fourier Transform, similar short-time Fourier transform.
- **`NSGT`** - Non-Stationary Gabor Transform.
- **`CWT`** - Continuous Wavelet Transform.
- **`PWT`** - Pseudo Wavelet Transform.
The above transform supports all the following frequency scale types:
- Linear - Short-time Fourier transform spectrogram.
- Linspace - Linspace-scale spectrogram.
- Mel - Mel-scale spectrogram.
- Bark - Bark-scale spectrogram.
- Erb - Erb-scale spectrogram.
- Octave - Octave-scale spectrogram.
- Log - Logarithmic-scale spectrogram.
The following transform are not supports multiple frequency scale types, only used as independent transform:
- **`CQT`** - Constant-Q Transform.
- **`VQT`** - Variable-Q Transform.
- **`ST`** - S-Transform/Stockwell Transform.
- **`FST`** - Fast S-Transform.
- **`DWT`** - Discrete Wavelet Transform.
- **`WPT`** - Wave Packet Transform.
- **`SWT`** - Stationary Wavelet Transform.
Detailed transform function, description, and use view the documentation.
The *_synchrosqueezing_* or *_reassignment_* is a technique for sharpening a time-frequency representation, contains the
following algorithms:
- **`reassign`** - reassign transform for `STFT`.
- **`synsq`** - reassign data use `CWT` data.
- **`wsst`** - reassign transform for `CWT`.
#### 2. Feature
The feature module contains the following algorithms:
- **`spectral`** - Spectrum feature, supports all spectrum types.
- **`xxcc`** - Cepstrum coefficients, supports all spectrum types.
- **`deconv`** - Deconvolution for spectrum, supports all spectrum types.
- **`chroma`** - Chroma feature, only supports `CQT` spectrum, Linear/Octave spectrum based on `BFT`.
#### 3. MIR
The mir module contains the following algorithms:
- **`pitch`** - YIN, STFT, etc algorithm.
- **`onset`** - Spectrum flux, novelty, etc algorithm.
- **`hpss`** - Median filtering, NMF algorithm.
## Installation
The library is cross-platform and currently supports Linux, macOS, Windows, iOS and Android systems.
### Python Package Install
To install the **audioFlux** package, Python >=3.6, using the released python package.
Using PyPI:
```
$ pip install audioflux
```
Using Anaconda:
```
$ conda install -c tanky25 -c conda-forge audioflux
```
### Other Build
- [iOS build](docs/installing.md#ios-build)
- [Android build](docs/installing.md#android-build)
- [Building from source](docs/installing.md#building-from-source)
## Quickstart
- [Mel & MFCC](docs/examples.md#mel--mfcc)
- [CWT & Synchrosqueezing](docs/examples.md#cwt--synchrosqueezing)
- [CQT & Chroma](docs/examples.md#cqt--chroma)
- [Different Wavelet Type](docs/examples.md#different-wavelet-type)
- [Spectral Features](docs/examples.md#spectral-features)
- [Pitch Estimate](docs/examples.md#pitch-estimate)
- [Onset Detection](docs/examples.md#onset-detection)
- [Harmonic Percussive Source Separation](docs/examples.md#harmonic-percussive-source-separation)
More example scripts are provided in the [Documentation](https://audioflux.top/) section.
## Benchmark
server hardware:
- CPU: AMD Ryzen Threadripper 3970X 32-Core Processor
More detailed performance benchmark are provided in the [Benchmark](https://github.com/libAudioFlux/audioFlux/tree/master/benchmark) module.
## Documentation
Documentation of the package can be found online:
[https://audioflux.top](https://audioflux.top/)
## Contributing
We are more than happy to collaborate and receive your contributions to **`audioFlux`**. If you want to contribute,
please fork the latest git repository and create a feature branch. Submitted requests should pass all continuous
integration tests.
You are also more than welcome to suggest any improvements, including proposals for need help, find a bug, have a
feature request, ask a general question, new algorithms.
Open an issue
## Citing
If you want to cite **`audioFlux`** in a scholarly work, please use the following ways:
- If you are using the library for your work, for the sake of reproducibility, please cite the version you used as
indexed at Zenodo:
[](https://doi.org/10.5281/zenodo.7548288)
## License
audioFlux project is available MIT License.