Name
Abstract | ||
|---|---|---|
In this paper we present a way to annotate music collec- tions by exploiting audio similarity. Similarity is used to propose labels (tags) to yet unlabeled songs, based on the content-based distance between them. The main goal of our work is to ease the process of annotating huge music collections, by using content-based similarity distances as a way to propagate labels among songs. We present two different experiments. The first one propagateslabelsthatarerelatedwiththestyleofthepiece, whereas the second experiment deals with mood labels. On the one hand, our approach shows that using a mu- sic collection annotated at 40% with styles, the collection can be automatically annotated up to 78% (that is, 40% already annotated and the rest, 38%, only using propaga- tion), with a recall greater than 0.4. On the other hand, for a smaller music collection annotated at 30% with moods, the collection can be automatically annotated up to 65% (e.g. 30% plus 35% using propagation). |
Year | Venue | Field |
|---|---|---|
2007 | ISMIR 2013 | Information retrieval,Computer science,Recall |
DocType | Citations | PageRank |
Conference | 23 | 1.72 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Sordo | 1 | 152 | 11.63 |
| Cyril Laurier | 2 | 236 | 13.42 |
| Oscar Celma | 3 | 28 | 2.23 |