Name
Title | ||
|---|---|---|
Feature Extraction Assessment For An Acoustic-Event Classification Task Using The Entropy Triangle |
Abstract | ||
|---|---|---|
We assess the behaviour of 5 different feature extraction methods for an acoustic event classification task-built using the same SVM underlying technology-by means of two different techniques: accuracy and the entropy triangle. The entropy triangle is able to find a classifier instance whose relatively high accuracy stems from an attempt to specialize in some classes to the detriment of the overall behaviour. On all other cases, fair classifiers, accuracy and entropy triangle agree. |
Year | Venue | Keywords |
|---|---|---|
2011 | 12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5 | Classifier evaluation, accuracy entropy triangle, acoustic event classification, SVM classifiers |
Field | DocType | Citations |
Pattern recognition,Computer science,Feature extraction,Speech recognition,Artificial intelligence | Conference | 4 |
PageRank | References | Authors |
0.47 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Mejía-Navarrete | 1 | 4 | 0.47 |
| j maciasguarasa | 2 | 92 | 19.30 |
| Carmen Peláez-moreno | 3 | 130 | 22.07 |
| Francisco J. Valverde-Albacete | 4 | 116 | 20.84 |