Name
Title | ||
|---|---|---|
Genre classification for million song dataset using confidence-based classifiers combination |
Abstract | ||
|---|---|---|
We proposed a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, we trained sub-classifiers by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, the genre labels for the Million Song Dataset are provided. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2348283.2348480 | SIGIR |
Keywords | Field | DocType |
classifier authority,million song dataset,concatenated vector,classification confidence,confidence-based classifiers combination,genre label,trained sub-classifiers,svm classifier,combined classifier,data type,song genre,genre classification | Pattern recognition,Computer science,Data type,Concatenation,Artificial intelligence,Svm classifier,Classifier (linguistics) | Conference |
Citations | PageRank | References |
3 | 0.38 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yajie Hu | 1 | 68 | 4.59 |
| Mitsunori Ogihara | 2 | 3135 | 257.04 |