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CI Methods for Music Information Retrieval
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Optimization of music classification chain
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Intelligent feature selection methods
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Learning of personal music categories
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Fuzzy methods using high-level feature analysis
Development of AMUSE (Advanced MUSic Explorer)
Visit GitHub development page of AMUSE
Description
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Java open source framework for different MIR tasks
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Multi-tool extendable environment with shared interfaces and data interchange formats
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Algorithm evaluation and optimization methods
Music Test Database
For the evaluation of music classification tasks we use a database of 120 commercial music albums purchased for our research group. The CDs are distributed among 6 AllMusicGuide genres:
| Classic | Electronica | Jazz | Pop/Rock | R&B | Rap | |
|---|---|---|---|---|---|---|
| CD number | 15 | 15 | 15 | 45 | 15 | 15 |
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Test set OS120 (optimization set for experiments, with focus on evolutionary multi-objective feature selection)
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Test set TS120 (song-independent test set from the same albums as OS120 for the evaluation of optimized models)
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Test set TAS120 (artist-independent test set with the same genre distribution)
The following audio features are used for classification (for definitions and the difference between low-level and high-level features see https://eldorado.tu-dortmund.de/handle/2003/30402)
Instrument Sample Database
Currently, the following instrument samples are used for the experiments on instrument recognition: