Package: audioflux
audioFluxaudiofluxis 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 Featuresv0.1.8Add a variety of Pitch algorithms:YIN,CEP,PEF,NCF,HPS,LHS,STFTandFFP.AddPitchShiftandTimeStretchalgorithms.Table of ContentsOverviewInstallationPython Package InstallOther BuildQuickstartBenchmarkDocumentationContributingCitingLicenseOverviewaudioFluxis 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 ofaudioFluxincludetransform,featureandmirmodules.1. TransformIn 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.Thesynchrosqueezingorreassignmentis a technique for sharpening a time-frequency representation, contains the
following algorithms:reassign- reassign transform forSTFT.synsq- reassign data useCWTdata.wsst- reassign transform forCWT.2. FeatureThe 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 supportsCQTspectrum, Linear/Octave spectrum based onBFT.3. MIRThe mir module contains the following algorithms:pitch- YIN, STFT, etc algorithm.onset- Spectrum flux, novelty, etc algorithm.hpss- Median filtering, NMF algorithm.InstallationThe library is cross-platform and currently supports Linux, macOS, Windows, iOS and Android systems.Python Package InstallTo install theaudioFluxpackage, Python >=3.6, using the released python package.Using PyPI:$ pip install audiofluxUsing Anaconda:$ conda install -c tanky25 -c conda-forge audiofluxOther BuildiOS buildAndroid buildBuilding from sourceQuickstartMel & MFCCCWT & SynchrosqueezingCQT & ChromaDifferent Wavelet TypeSpectral FeaturesPitch EstimateOnset DetectionHarmonic Percussive Source SeparationMore example scripts are provided in theDocumentationsection.Benchmarkserver hardware:- CPU: AMD Ryzen Threadripper 3970X 32-Core ProcessorMore detailed performance benchmark are provided in theBenchmarkmodule.DocumentationDocumentation of the package can be found online:https://audioflux.topContributingWe are more than happy to collaborate and receive your contributions toaudioFlux. 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 issueCitingIf you want to citeaudioFluxin 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:LicenseaudioFlux project is available MIT License.