A library for audio and music analysis, feature extraction.
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.
Table of Contents
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.
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
synsq- reassign data use
wsst- reassign transform for
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
CQTspectrum, Linear/Octave spectrum based on
The mir module contains the following algorithms:
pitch- YIN, STFT, etc algorithm.
onset- Spectrum flux, novelty, etc algorithm.
hpss- Median filtering, NMF algorithm.
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.
$ pip install audioflux
$ conda install -c tanky25 -c conda-forge audioflux
- Mel & MFCC
- CWT & Synchrosqueezing
- CQT & Chroma
- Different Wavelet Type
- Spectral Features
- Pitch Estimate
- Onset Detection
- Harmonic Percussive Source Separation
More example scripts are provided in the Documentation section.
- CPU: AMD Ryzen Threadripper 3970X 32-Core Processor
More detailed performance benchmark are provided in the Benchmark module.
Documentation of the package can be found online:
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
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
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:
audioFlux project is available MIT License.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for audioflux-0.1.6-py3-none-any.whl