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audeep

auDeep  

A Python toolkit for unsupervised feature learning with deep neural networks (DNNs).

Developers:  Shahin Amiriparian, Michael Freitag, Sergey Pugachevskiy, Björn W. Schuller
GitHub  https://github.com/auDeep/auDeep

auDeep is a Python toolkit for unsupervised feature learning with deep neural networks (DNNs). Currently, the main focus of this project is feature extraction from audio data with deep recurrent autoencoders. However, the core feature learning algorithms are not limited to audio data. Furthermore, we plan on implementing additional DNN-based feature learning approaches.

(c) 2017 Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Björn Schuller: Universität Passau 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 Michael Freitag (freitagm at fim.uni-passau.de).

Citing

If you use auDeep or any code from auDeep in your research work, you are kindly asked to acknowledge the use of auDeep in your publications.

M. Freitag, S. Amiriparian, S. Pugachevskiy, N. Cummins, and B.Schuller. auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks, Journal of Machine Learning Research, 2017, submitted, 5 pages.

S. Amiriparian, M. Freitag, N. Cummins, and B. Schuller. Sequence to sequence autoencoders for unsupervised representation learning from audio, Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop, pp. 17-21, 2017.


DeepSpectrum

a Python toolkit for feature extraction from audio data with pre-trained Image Convolutional Neural Networks (CNNs).

Developers:  Shahin Amiriparian, Maurice Gerczuk, Sandra Ottl, Björn W. Schuller
GitHub  https://github.com/DeepSpectrum/DeepSpectrum


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) 2017-2018 Shahin Amiriparian, Maurice Gercuk, 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 (gerczuk at fim.uni-passau.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).


open-XBOW

openXBOW 

The Passau Open-Source Crossmodal Bag-of-Words Toolkit

Authors Maximilian Schmitt, Björn W. Schuller
GitHub  https://github.com/openXBOW/openXBOW

openXBOW generates a bag-of-words representation from a sequence of numeric and/or textual features, e.g., acoustic LLDs, visual features, and transcriptions of natural speech. The tool provides a multitude of options, e.g., different modes of vector quantisation, codebook generation, term frequency weighting and methods known from natural language processing. In the GitHub repository, you find a tutorial that helps you to starting working with openXBOW.

The development of this toolkit has been supported by the European Union's Horizon 2020 Programme under grant agreement No. 645094 (IA SEWA) and the European Community's Seventh Framework Programme through the ERC Starting Grant No. 338164 (iHEARu). SEWA iHEARu EU Horizon2020

For more information, please visit the official websites: http://sewaproject.eu http://ihearu.eu (C) 2016-2017, published under GPL v3, please check the file LICENSE.txt for details. Maximilian Schmitt, Björn Schuller: University of Passau. Contact: maximilian.schmitt@uni-passau.de

Citing

If you use openXBOW or any code from openXBOW in your research work, you are kindly asked to acknowledge the use of openXBOW in your publications.

http://www.jmlr.org/papers/v18/17-113.html

Maximilian Schmitt, Björn W. Schuller: openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit, Journal of Machine Learning Research, vol. 18, pp. 1-5, 2017.