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This is in spite of the cautions Silla et al. Through my fingerprinting method (just a little Shazam-like implementation), I compare all songs in each class, and find 213 replicas. I have acquired the audio for LMD, which has 3,229 song files (two more than stated by Silla et al.). However, as for GTZAN, and as for Ballroom, it appears that researchers have taken for granted the integrity of LMD. Systems, Man, and Cybernetics, San Antonio, USA, Oct. Novel top-down approaches for hierarchical classification and their application to automatic music genre classification. In Jaime Sichman, Helder Coelho, and Solange Rezende, editors, Advances in Artificial Intelligence, pages 339-348. Time-space ensemble strategies for automatic music genre classification.
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A feature selection approach for automatic music genre classification. Feature selection in automatic music genre classification. Automatic music genre classification using ensembles of classifiers. Improving automatic music genre classification with hybrid content-based feature vectors. Capturing the temporal domain in echonest features for improved classification effectiveness. Additional evidence that common low-level features of individual audio frames are not representative of music genres. Short-term feature space and music genre classification. Selection of training instances for music genre classification. On the suitability of state-of-the-art music information retrieval methods for analyzing, categorizing and accessing non-western and ethnic music collections. Modified AIS-based classifier for music genre classification. Wieczorkowska, editors, Advances in Music Information Retrieval, pages 390-402. Automatic musical genre classification and artificial immune recognition system. Music genre classification using LBP textural features. Comparing textural features for music genre classification. Systems, Signals and Image Process., 2011. Music genre recognition using spectrograms. Searching through the references of my music genre recognition survey, I find this dataset (or portions of it) has been used in the evaluations of music genre recognition systems in at least 16 conference papers and journal articles: However, unlike GTZAN and Ballroom, the audio data is not freely available only pre-computed features are available for download. Like the Ballroom dataset, each music recording is assigned a single label by “experts in Brazilian dance” according to the appropriate dance. This dataset is notable among those created for music genre recognition because it contains music outside the realm of Western popular music. That paper describes the LMD as 3,227 song recordings, each labeled in one of ten different classes: Axé, Batchata, Bolero, Forró, Gaúcha, Merengue, Pagode, Salsa, Sertaneja, and Tango. Kaestner, “The Latin music database,” in Proc. It has been used in the MIREX Latin music genre recognition task since 2009.
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for use in a comparative evaluation of particular approaches for music genre classification. The Latin Music Database (LMD) was created around 2007 by Silla et al.