Name
Abstract | ||
|---|---|---|
This paper proposes a speech/music classification system based on i-vector. An analysis of two classification methods, namely cosine distance score (CDS) and support vector machine (SVM) is performed. Two session compensation methods, within-class covariance normalization (WCCN) and linear discriminant analysis (LDA) are also discussed. The performance of proposed systems yields better results compared with Gaussian mixture model (GMM) method and modified low energy ratio (MLER) method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ISSPIT.2016.7885999 | 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) |
Keywords | Field | DocType |
speech/music classification,i-vector,support vector machine (SVM) | Kernel (linear algebra),Compensation methods,Normalization (statistics),Pattern recognition,Computer science,Support vector machine,Feature extraction,Speech recognition,Artificial intelligence,Linear discriminant analysis,Mixture model,Covariance | Conference |
ISSN | ISBN | Citations |
2162-7843 | 978-1-5090-5845-7 | 1 |
PageRank | References | Authors |
0.38 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Zhang | 1 | 203 | 64.03 |
| Xu-Kui Yang | 2 | 15 | 2.69 |
| Wei-Qiang Zhang | 3 | 136 | 31.22 |
| Wen-Lin Zhang | 4 | 6 | 3.56 |
| Jia Liu | 5 | 277 | 50.34 |