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The auditory modeling toolbox (AMT) is a Matlab/Octave toolbox for the development and application of auditory computational models. Over 50 auditory models implemented in Matlab, Octave, C, C++, and Python can be run from Matlab and Octave, on Windows and Linux.
The AMT provides a well-structured in-code documentation, includes auditory data required to run the models.
FEATool Multiphysics is an easy-to-use FEA and CFD Simulation Toolbox
FEATool Multiphysics (https://www.featool.com) is a fully integrated toolbox for computer aided engineering CAE, finite element analysis & fluid dynamics simulations.
With a very easy-to-use GUI, anyone is now able to quickly set up and perform large scale dynamical and complex engineering physics simulations, with coupled fluid flow, heat transfer, structural mechanics, chemical transport, and electromagnetics effects, without having to learn complex programming.
Yet Another Audio Feature Extractor is a toolbox for audio analysis. Easy to use and efficient at extracting a large number of audio features simultaneously. WAV and MP3 files supported, or embedding in C++, Python or Matlab applications.
This project, developed at UCL London, provides code for tomographic reconstruction. NiftyRec is written in C and has Python and Matlab extensions. Computationally intensive functions have a GPU accelerated version based on CUDA.
psignifit is a toolbox to fit psychometric functions and test hypotheses on psychometric data. This is version 3 which will now predominantly support python.
C++, Matlab and Python library for Hidden-state Conditional Random Fields. Implements 3 algorithms: LDCRF, HCRF and CRF. For Windows and Linux, 32- and 64-bits. Optimized for multi-threading. Works with sparse or dense input features.
Discrete wavelet methods for time series analysis using python
...This library aims at filling this gap, in particular considering discrete wavelet transform as described by Percival and Walden.
This module started as translation of the wmtsa Matlabtoolbox (http://www.atmos.washington.edu/~wmtsa/), so most naming conventions and most of the code structure follows their choices. The code uses a mix of python and cython for improved performance.
The code reflects my needs and preferences, but contributions from others are welcome. The code has to some extent been tested, but bugs are to be expected.
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