Metadata-Version: 2.1
Name: DeepSpectrum
Version: 0.6.7
Summary: UNKNOWN
Home-page: https://github.com/DeepSpectrum/DeepSpectrum
Author: Maurice Gerczuk
Author-email: gerczuk@fim.uni-passau.de
License: GPLv3+
Project-URL: Source, https://github.com/DeepSpectrum/DeepSpectrum/
Project-URL: Tracker, https://github.com/DeepSpectrum/DeepSpectrum/issues
Keywords: machine-learning audio-analysis science research
Platform: Any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: GPU :: NVIDIA CUDA :: 10.0
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.7
Requires-Python: ~=3.7.2,
Description-Content-Type: text/markdown
Requires-Dist: audeep (>=0.9.4)
Requires-Dist: imread (>=0.7.0)
Requires-Dist: tqdm (>=4.30.0)
Requires-Dist: matplotlib (>=3.3)
Requires-Dist: numba (==0.48.0)
Requires-Dist: librosa (<0.8.0,>=0.7.0)
Requires-Dist: click (>=7.0)
Requires-Dist: Pillow (>=6.0.0)
Requires-Dist: tensorflow-gpu (<2,>=1.15.2)
Requires-Dist: opencv-python (>=4.0.0.21)
Requires-Dist: torch (>=1.2.0)
Requires-Dist: torchvision (>=0.5.0)

**DeepSpectrum** is a Python toolkit for feature extraction from audio data with pre-trained Image Convolutional Neural Networks (CNNs). It features an extraction pipeline which first creates visual representations for audio data - plots of spectrograms or chromagrams - and then feeds them to a pre-trained Image CNN. Activations of a specific layer then form the final feature vectors.

**(c) 2017-2020 Shahin Amiriparian, Maurice Gerczuk, Sandra Ottl, Björn Schuller: Universität Augsburg**
Published under GPLv3, see the LICENSE.md file for details.

Please direct any questions or requests to Shahin Amiriparian (shahin.amiriparian at tum.de) or Maurice Gercuk (maurice.gerczuk at informatik.uni-augsburg.de).

# Citing
If you use DeepSpectrum or any code from DeepSpectrum in your research work, you are kindly asked to acknowledge the use of DeepSpectrum in your publications.
> S. Amiriparian, M. Gerczuk, S. Ottl, N. Cummins, M. Freitag, S. Pugachevskiy, A. Baird and B. Schuller. Snore Sound Classification using Image-Based Deep Spectrum Features. In Proceedings of INTERSPEECH (Vol. 17, pp. 2017-434)