<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kurt Jacobson</style></author><author><style face="normal" font="default" size="100%">Matthew Davies</style></author><author><style face="normal" font="default" size="100%">Mark Sandler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Textual Annotation of Rhythmic Style in Electronic Dance Music</style></title><secondary-title><style face="normal" font="default" size="100%">123rd AES Convention</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October, 2007</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">New York, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Music information retrieval encompasses a complex and diverse set of problems. Some recent work has focused on automatic textual annotation of audio data, paralleling work in image retrieval. Here we take a narrower approach to the automatic textual annotation of music signals and focus on rhythmic style. Training data for rhythmic styles are derived from simple, precisely labeled drum loops intended for content creation. These loops are already textually annotated with the rhythmic style they represent. The training loops are then compared against a database of music content to apply textual annotations of rhythmic style to unheard music signals. Three distinct methods of rhythmic analysis are explored. These methods are tested on a small collection of electronic dance music resulting in a labeling accuracy of 73%.</style></abstract></record></records></xml>