Learnng Sparse Feature Representations For Music Annotation And Retrieval

Submitted by jorgeh on Wed, 08/15/2012 - 5:36pm

Title	Learnng Sparse Feature Representations For Music Annotation And Retrieval
Publication Type	Conference Paper
Year of Publication	2012
Authors	Nam, J., J. Herrera, M. Slaney, and J. Smith
Conference Name	13th International Society for Music Information Retrieval Conference
Date Published	10/2012
Conference Location	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
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Learnng Sparse Feature Representations For Music Annotation And Retrieval

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