Music genre classification using explicit semantic analysis

Kamelia Aryafar, Ali Shokoufandeh
<span title="">2011</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lahlxihmo5fhzpexw7rundu24u" style="color: black;">Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies - MIRUM &#39;11</a> </i> &nbsp;
Motivation We are interested in automatically finding genre labels for a music data set. The collection of user protocols is common in human factors research, but analyzing the large data sets produced can be tedious. Recent work has proposed the use of an automated method based on explicit semantic analysis to identify the most representative genre patterns in a large data set. The method only uses signal-based mel frequency cepstral coefficients (MFCCs) as audio feature representation and
more &raquo; ... oves upon previous methods that use the same set of features for music genre classification.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2072529.2072539">doi:10.1145/2072529.2072539</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/mm/AryafarS11.html">dblp:conf/mm/AryafarS11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xlaf7dx4wjdffkbi5j4ah5eit4">fatcat:xlaf7dx4wjdffkbi5j4ah5eit4</a> </span>
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