Name
Title | ||
|---|---|---|
Homogeneous segmentation and classifier ensemble for audio tag annotation and retrieval |
Abstract | ||
|---|---|---|
Audio tags describe different types of musical information such as genre, mood, and instrument. This paper aims to automatically annotate audio clips with tags and retrieve relevant clips from a music database by tags. Given an audio clip, we divide it into several homogeneous segments by using an audio novelty curve, and then extract audio features from each segment with respect to various musical information, such as dynamics, rhythm, timbre, pitch, and tonality. The features in frame-based feature vector sequence format are further represented by their mean and standard deviation such that they can be combined with other segment-based features to form a fixed-dimensional feature vector for a segment. We train an ensemble classifier, which consists of SVM and AdaBoost classifiers, for each tag. For the audio annotation task, the individual classifier outputs are transformed into calibrated probability scores such that probability ensemble can be employed. For the audio retrieval task, we propose using ranking ensemble. We participated in the MIREX 2009 audio tag classification task and our system was ranked first in terms of F-measure and the area under the ROC curve given a tag. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICME.2010.5583009 | ICME |
Keywords | Field | DocType |
audio tag annotation,ensemble method,frame-based feature vector sequence format,adaboost classifier,homogeneous segmentation,audio novelty curve,information retrieval,audio segmentation,svm classifier,audio tag retrieval,music database,ensemble classifier,audio feature extraction,feature extraction,signal classification,f-measure,calibrated probability scores,audio signal processing,mirex 2009 audio tag classification task,ranking ensemble,classifier ensemble,support vector machines,probability ensemble,roc curve,probability,f measure,standard deviation,accuracy,classification algorithms,feature vector,measurement | Feature vector,AdaBoost,Pattern recognition,Computer science,Audio mining,Support vector machine,Feature extraction,Speech recognition,Artificial intelligence,Classifier (linguistics),Statistical classification,Audio signal processing | Conference |
ISSN | ISBN | Citations |
1945-7871 | 978-1-4244-7491-2 | 9 |
PageRank | References | Authors |
0.60 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hung-Yi Lo | 1 | 118 | 8.33 |
| Ju-Chiang Wang | 2 | 215 | 14.46 |
| Hsin-min Wang | 3 | 1201 | 129.62 |