Mir tools

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The Tools We Use
Version 1.5
May 2005
 Since October 2004, I have been surveying the MIR community (via the music-ir@ircam.fr mailing list)  as to what tools MIR researchers were using.   I have compiled this list based upon the inputs of MIR researchers from around the world.   I'd like to keep this list active and up to date, so if you know of any tools that you think should be on this listlet me know.  Thanks for all the input!    -- Paul Lamere: (email, blog) -  Sun Labs. 
Machine Learning
* M2K - M2K represents the music-specific set of D2K modules designed to create a Virtual Research Lab (VRL) for MIR/MDL development, prototyping and evaluation.  M2K provides the framework for the MIREX (Music Information Retrieval Evaluation eXchange) contest, an annual MIR evaluation.D2K, together with a subsidiary set of modules called T2K (Text-to-Knowledge), provide the basic foundation upon which M2K is being developed. D2K/T2K are the result of a ongoing research and development project of the Automated Learning Group (ALG) at NCSA.  M2K License: BSD-Like
* Weka - Weka is a collection of machine learning algorithms for data mining tasks written in the Java programminglanguage. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. The book: Data Mining compliments the Weka Software. License: GNU General Public License (GPL) .
* Marsyas - Marsyas is a software framework for rapid prototyping and experimentation withcomputer audition applications with specific emphasis on Music Information Retrieval. Marsyas  provides a general, extensible and flexible architecture that allows easy experimentation with algorithms and provides fast performance that is useful in developing real time audio analysis tools.   A variety of existing building blocks that form the basis of most published algorithms in Computer Audition arealready available as part of the package.  Marsyas is written in C++ and Java and is actively being developed by George Tzanetakis.  License: GNU General Public License (GPL)

* Torch - Torch is a machine learning library written in C++ that works on most Unix/Linux platforms. It can be used to train MLPs, RBFs, HMMs, Gaussian Mixtures, Kmeans, Mixtures of experts, Parzen Windows, KNN, and canbe easily extended so that you can add your own machine learning algorithms. Torch is currently developed at IDIAP and is described in the paper Torch: a modular machine learning software library  Torch3 has been successfully tested on Linux, SunOS, FreeBSD, OSF1, Mac OS X and even MS Windows. License: Torch3 is free, distributed under a BSD license.
* NODElib - Neural OptimizationDevelopment Engine library is a programming library for rapidly developing powerful neural network simulations. The code is extremely modular, compact, and robust. It is written in an object oriented manner. All of the library code, example and test program source,w documentation, and supporting text is only on the order of about 20,000 lines, which means that NODElib is extremely compact. NODELib iswritten in C.  License: GNU General Public License (GPL) .
* SVM - this package defines support vector machines (SVMs) for both classification and regression problems. The SVMs can use a wide variety of kernel functions. Optimization of the SVMs is performed by a variation of John Platt's sequential minimal optimization (SMO) algorithm. This version of SMO is generalized for regression, uses kernelcaching, and incorporates several heuristics; for these reasons, we refer to the optimization algorithm as SMORCH. SMORCH has been shown to be over an order magnitude faster than SMO, QP, and decomposition. License: GNU General Public License (GPL) .
* LAPACK/BLAS (Linux version available from Intel) for matrix math - The BLAS (Basic Linear Algebra Subprograms) are high quality "building...
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