Metadata-Version: 2.4
Name: essentia-tensorflow
Version: 2.1b6.dev1438
Summary: Library for audio and music analysis, description and synthesis, with TensorFlow support
Home-page: http://essentia.upf.edu
Author: Dmitry Bogdanov
Author-email: dmitry.bogdanov@upf.edu
License-Expression: AGPL-3.0-only
Project-URL: Documentation, http://essentia.upf.edu
Project-URL: Source Code, https://github.com/MTG/essentia
Keywords: audio music sound dsp MIR
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Topic :: Multimedia :: Sound/Audio :: Sound Synthesis
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
License-File: COPYING.txt
License-File: AUTHORS
Requires-Dist: numpy>=1.25
Requires-Dist: pyyaml
Requires-Dist: six
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Dynamic: description
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Essentia is an open-source C++ library with Python bindings for audio analysis and audio-based music information retrieval. It contains an extensive collection of algorithms, including audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, a large variety of spectral, temporal, tonal, and high-level music descriptors, and tools for inference with deep learning models. Designed with a focus on optimization in terms of robustness, computational speed, low memory usage, as well as flexibility, it is efficient for many industrial applications and allows fast prototyping and setting up research experiments very rapidly.

Website: https://essentia.upf.edu
