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Dismiss alert {{ message }} libAudioFlux / audioFlux Public * Notifications You must be signed in to change notification settings * Fork 110 * Star 2.4k A library for audio and music analysis, feature extraction. audioflux.top License MIT license 2.4k stars 110 forks Branches Tags Activity Star Notifications You must be signed in to change notification settings * Code * Issues 13 * Pull requests 0 * Discussions * Actions * Projects 0 * Security * Insights Additional navigation options * Code * Issues * Pull requests * Discussions * Actions * Projects * Security * Insights libAudioFlux/audioFlux This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. master BranchesTags Go to file Code Folders and files Name Name Last commit Last commit message date Latest commit History 165 Commits .github/workflows .github/ workflows benchmark benchmark conda conda docs docs image image include include python/audioflux python/audioflux scripts scripts src src .gitignore .gitignore LICENSE.md LICENSE.md README.md README.md requirements.txt requirements.txt setup.py setup.py View all files Repository files navigation * README * MIT license [logo] audioFlux GitHub Workflow Status (with branch) example branch parameter language PyPI - Version PyPI - Python Version Docs GitHub DOI 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 * Installation + Python Package Install + Other Build * Quickstart * Benchmark * Documentation * Contributing * Citing * 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. [feature_al] 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. [flow] 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 language 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 * Android build * Building from source Quickstart * 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. Benchmark server hardware: - CPU: AMD Ryzen Threadripper 3970X 32-Core Processor [linux_amd_1] More detailed performance benchmark are provided in the Benchmark module. Documentation Documentation of the package can be found online: 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: DOI License audioFlux project is available MIT License. About A library for audio and music analysis, feature extraction. audioflux.top Topics audio python music machine-learning deep-learning signal-processing audio-features audio-analysis music-information-retrieval spectrogram mfcc pitch mir spectral-analysis music-analysis audio-processing wavelet-analysis wavelet-transform time-frequency-analysis Resources Readme License MIT license Activity Custom properties Stars 2.4k stars Watchers 32 watching Forks 110 forks Report repository Releases 10 v0.1.9 Latest May 24, 2024 + 9 releases Packages 0 No packages published Contributors 5 * @wtq2255 * @liufeigit * @LiweiDong * @vanvcool * @johanez Languages * C 62.6% * Python 36.9% * Other 0.5% Footer (c) 2024 GitHub, Inc. Footer navigation * Terms * Privacy * Security * Status * Docs * Contact * Manage cookies * Do not share my personal information You can't perform that action at this time.