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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