@conference {3480,
	title = {Learnng Sparse Feature Representations For Music Annotation And Retrieval},
	booktitle = {13th International Society for Music Information Retrieval Conference},
	year = {2012},
	note = {\&nbsp;},
	month = {10/2012},
	address = {Porto, Portugal},
	abstract = {We present a data-processing pipeline based on sparse feature learning and describe its applications to music annotation and retrieval. Content-based music annotation and retrieval systems process audio starting with features. While commonly used features, such as MFCC, are handcrafted to extract characteristics of the audio in a succinct way, there is increasing interest in learning features automatically from data using unsupervised algorithms. We describe a systemic approach applying feature-learning algorithms to music data, in particular, focusing on a highdimensional sparse-feature representation. Our experiments show that, using only a linear classifier, the newly learned features produce results on the CAL500 dataset comparable to state-of-the-art music annotation and retrieval systems.},
	url = {http://ccrma.stanford.edu/~juhan/pubs/jnam-ismir2012.pdf},
	author = {Juhan Nam and Jorge Herrera and Slaney, Malcolm and Smith, Julius}
}
