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Task - ISMIS 2011 Contest: Music Information Retrieval - Instruments | TunedIT

http://tunedit.org/challenge/music-retrieval/instruments

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. ISMIS 2011 Contest: Music Information Retrieval. It is fairly easy to automatically recognize single instruments. However, when more than one instrument plays at the same time, the task becomes much more difficult. In this track, the goal is to build a model based on data collected for both single instruments and example mixtures in order to recognize pairs of instruments. MFCC1-13 - MFCC coefficients.

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Data mining challenges, competition, contest | TunedIT

http://tunedit.org/challenges

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. Scientific and Industrial (11). Our dataset represents object recognition in the olfactory domain. The testing data comprises the activity of a simulated gas sensor "traversing" an environment containing multiple odorant sources, as well as a persistent background odorant. In the training data the sensor is traversing the. FIND Technologies Inc. is a Canadian company that owns novel sensor techno...

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New Challenge | TunedIT

http://tunedit.org/newChallenge

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. Title that will appear on the challenge page and on the list of all challenges. May contain any characters. Short name that will be used to create URL of the challenge page. May contain ascii letters, digits, dots, dashes and underlines. Path to the folder in Repository where challenge files (datasets, solutions, .). Together with all intermediate that do not exist. Path to the folder in Repository w...

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Tunedtester | TunedIT

http://tunedit.org/tunedtester

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. Learn more ». You can download TunedTester from Repository as tunedtester.zip. Or tunedtester.tar.gz. Look for Java SE Runtime Environment entry. When running TunedTester manually from the console as "java .", please remember NOT to omit the JVM parameter -Djava.security.policy=security.policy. Otherwise the security sandbox for test execution will be turned off. Also, make sure that the command ...

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Knowledge Base: performance results, benchmarks, tests | TunedIT

http://tunedit.org/results

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. Star * matches any sequence of characters. Leading and trailing star is required only. Several alternative patterns can be given,. 1) letter pendigits OCR segment. 2) *breast*.arff *hepatitis.arff UCI/*cholesterol*. Class name is a part of full resource name. It is given after colon:. 1) Weka/weka-3.6.1.jar:weka.classifiers.lazy.KStar. 4) neural rbf perceptron. Any" will be matched by.

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Repository of data sets and algorithms | TunedIT

http://tunedit.org/repo

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. You must be logged in. To upload new resources. Various bioinformatics datasets converted to ARFF by Jesús S. Aguilar-Ruiz and BioInformatics Group Seville (BIGS). Datasets of Data And Story Library, project illustrating use of basic statistic methods, converted to arff format by Hakan Kjellerstrand. Example implementations of algorithms that can be tested with TunedTester.

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Task - ISMIS 2011 Contest: Music Information Retrieval - Genres | TunedIT

http://tunedit.org/challenge/music-retrieval/genres

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. ISMIS 2011 Contest: Music Information Retrieval. The feature vector consists of 191 parameters, the first 127 parameters are based on the MPEG-7 standard, the remaining ones are cepstral coefficients descriptors and time-related dedicated parameters:. A) parameter 1: Temporal Centroid,. B) parameter 2: Spectral Centroid average value,. C) parameter 3: Spectral Centroid variance,. L) parameters 103-12...

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IEEE ICDM Contest: TomTom Traffic Prediction for Intelligent GPS Navigation | TunedIT

http://tunedit.org/challenge/IEEE-ICDM-2010

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. IEEE ICDM Contest: TomTom Traffic Prediction for Intelligent GPS Navigation. The challenge is over now. Click here to view the Summary. Data mining competition being a part of IEEE International Conference on Data Mining 2010. ICDM), Sydney, Australia, Dec 14-17. Sponsored by TomTom. Held under the patronage of the President of Warsaw, Mrs. Hanna Gronkiewicz-Waltz. There are 3 tasks:. Traffic congest...

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TunedIT in Media | TunedIT

http://tunedit.org/media

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. 45 tys. dolarów za stworzenie algorytmu dla kanadyjskiej firmy. Competition: $45,000 for identification of substances from electromagnetic signatures. VideoLectures.net Recommender System Competition. European Network of Business and Industrial Statistics. IEEE data mining competition on city traffic. O warszawskich korkach w. Australii. Przewidywanie natężenia ruchu drogowego i korków ulicznych.

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ISMIS 2011 Contest: Music Information Retrieval | TunedIT

http://tunedit.org/challenge/music-retrieval

Machine Learning and Data Mining Algorithms. Automated Tests, Repeatable Experiments, Meaningful Results. ISMIS 2011 Contest: Music Information Retrieval. The challenge is over now. Click here to view the Summary. Data mining contest associated with the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011). The contest comprises 2 tracks:. Recognition of genre of music (jazz, rock, pop, .) from a short sample. Recognition of instruments playing together in a given sample.