Name
Abstract | ||
|---|---|---|
Automatic music classification and summarization are very useful to music indexing, content-based music retrieval and on-line music distribution, but it is a challenge to extract the most common and salient themes from unstructured raw music data. In this paper, we propose effective algorithms to automatically classify and summarize music content. Support vector machines are applied to classify music into pure music and vocal music by learning from training data. For pure music and vocal music, a number of features are extracted to characterize the music content, respectively. Based on calculated features, a clustering algorithm is applied to structure the music content. Finally, a music summary is created based on the clustering results and domain knowledge related to pure and vocal music. Support vector machine learning shows a better performance in music classification than traditional Euclidean distance methods and hidden Markov model methods. Listening tests are conducted to evaluate the quality of summarization. The experiments on different genres of pure and vocal music illustrate the results of summarization are significant and effective. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TSA.2004.840939 | IEEE Transactions on Speech and Audio Processing |
Keywords | Field | DocType |
euclidean distance method,vocal music,pattern clustering,content-based music retrieval,learning (artificial intelligence),music,automatic music classification,music classification,on-line music distribution,indexing,music indexing,hidden markov model method,support vector machine learning,feature extraction,music characterization,listening test,signal classification,clustering,audio signal processing,hidden markov models,content-based retrieval,music summarization,support vector machines,training data,data mining,euclidean distance,support vector machine,machine learning,indexing terms,clustering algorithms,hidden markov model,learning artificial intelligence,signal processing,domain knowledge,algorithm,information retrieval,indexation | Automatic summarization,Music information retrieval,Pattern recognition,Domain knowledge,Computer science,Support vector machine,Vocal music,Feature extraction,Speech recognition,Artificial intelligence,Hidden Markov model,Cluster analysis | Journal |
Volume | Issue | ISSN |
13 | 3 | 1063-6676 |
Citations | PageRank | References |
33 | 2.22 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changsheng Xu | 1 | 4957 | 332.87 |
| Namunu Chinthaka Maddage | 2 | 108 | 11.28 |
| Xi Shao | 3 | 226 | 19.08 |