Name
Abstract | ||
|---|---|---|
With independent component analysis (ICA) to realize the blind separation from mixed acoustic objects, a recognition method based on support vector machine/Gaussian mixture models (SVM/GMM) is proposed through extracting linear prediction coefficient (LPC) feature. It is revealed that LPC is consistently better than wavelet energy feature, ICA is efficient algorithm to estimate the unknown signal level. This method uses the output of GMM to adjust the probabilistic output of SVM. The validity of the ICA and SVM/GMM model is verified via examples in mixed acoustic objects recognition system. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72393-6_125 | ISNN (2) |
Keywords | Field | DocType |
independent component analysis,gaussian mixture model,recognition method,mixed acoustic objects recognition,probabilistic output,efficient algorithm,mixed acoustic object,gmm model,blind separation,wavelet energy feature,support vector machine,object recognition | Computer science,Artificial intelligence,Probabilistic logic,Discriminative model,Wavelet,Pattern recognition,Recognition system,Support vector machine,Speech recognition,Linear prediction coefficient,Independent component analysis,Machine learning,Mixture model | Conference |
Volume | ISSN | Citations |
4492 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaobo Li | 1 | 0 | 0.68 |
| Zhiliang Ren | 2 | 0 | 1.01 |
| Gong Chen | 3 | 58 | 8.46 |
| Changcun Sun | 4 | 0 | 0.68 |