Name
Abstract | ||
|---|---|---|
In this paper we present a method for the selection of training instances based on the classification accuracy of a SVM classifier. The instances consist of feature vectors representing short-term, low-level characteristics of music audio signals. The objective is to build, from only a portion of the training data, a music genre classifier with at least similar performance as when the whole data is used. The particularity of our approach lies in a pre-classification of instances prior to the main classifier training: i.e. we select from the training data those instances that show better discrimination with respect to class memberships. On a very challenging dataset of 900 music pieces divided among 10 music genres, the instance selection method slightly improves the music genre classification in 2.4 percentage points. On the other hand, the resulting classification model is significantly reduced, permitting much faster classification over test data. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.1128 | ICPR |
Keywords | Field | DocType |
training instances,music audio signal,training data,resulting classification model,music piece,faster classification,classification accuracy,music genre classification,music genre,main classifier training,music genre classifier,support vector machines,audio signal processing,feature vectors,accuracy,mir,feature vector,music,feature extraction,multiple signal classification | Audio signal,Music information retrieval,Feature vector,Pattern recognition,Computer science,Support vector machine,Feature extraction,Test data,Artificial intelligence,Audio signal processing,Classifier (linguistics) | Conference |
Citations | PageRank | References |
13 | 0.64 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miguel Lopes | 1 | 13 | 0.64 |
| Fabien Gouyon | 2 | 13 | 0.64 |
| Alessandro L. Koerich | 3 | 525 | 39.59 |
| Luiz S. Oliveira | 4 | 476 | 47.22 |