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http://www.ee.columbia.edu/~dpwe/ismir2004/CRFILES/paper211.pdf
classified into 5 genres using web-based data [29]. Second, we investigate the impact on the results of fluc-tuations over time of the retrieved content. For this ex-periment we retrieved the top ranked pages from search engines for 12 artists every other day for a period of 4 months. Third, we classify 224 artists into 14 genres (16 artists
https://www.researchgate.net/publication/220723814_Artist_Classification_with_Web-Based_Data
Artist Classification with Web-Based Data. January 2004; Source; DBLP; Conference: ISMIR 2004, 5th International Conference on Music Information Retrieval, Barcelona, Spain, October 10-14, 2004 ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.2045
BibTeX @INPROCEEDINGS{Knees04artistclassification, author = {Peter Knees and Elias Pampalk and Gerhard Widmer}, title = {Artist classification with web-based data}, booktitle = {In Proceedings of the 5th International Symposium on Music Information Retrieval (ISMIR’04}, year = {2004}, pages = {517- …
https://www.jku.at/fileadmin/gruppen/173/Research/Automatic_Classification_of.pdf
In this paper, we extract features for artists from web-based data and classify the artists with support vector machines (SVMs). In particular, we query Internet search engines with artist names combined with constraints such as +music +review and retrieve the top ranked pages. The retrieved pages tend to be common web pages such as fan pages,
http://core.ac.uk/display/23345986
Artist classification with web-based data . By Peter Knees, Elias Pampalk and Gerhard Widmer. Abstract. Manifold approaches exist for organization of music by genre and/or style. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked ...Author: Peter Knees, Elias Pampalk and Gerhard Widmer
https://www.researchgate.net/publication/228569738_Automatic_classification_of_musical_artists_based_on_web-data
The second approach to music genre classification is from cultural metadata which can be extracted from the Internet in the form of music reviews, Internet based searches (artists, music titles ...
https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0222-3
Jun 25, 2019 · An effective input format for comic classification is first defined, and a convolutional neural network is used to classify comic images into eight different artist categories. Using a publicly available dataset, the trained model obtains a mean F1 score of 84% for the classification.
https://dl.acm.org/doi/10.1145/1178677.1178699
Artist Classification with Web-based Data.In Proceedings of 5th International Conference on Music Information Retrieval (ISMIR '04), pages 517--524, Barcelona, Spain, October 2004. Google Scholar P. Knees, M. Schedl, T. Pohle, and G. Widmer.
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