Name
Title | ||
|---|---|---|
Music/speech classification using high-level features derived from fmri brain imaging |
Abstract | ||
|---|---|---|
With the availability of large amount of audio tracks through a variety of sources and distribution channels, automatic music/speech classification becomes an indispensable tool in social audio websites and online audio communities. However, the accuracy of current acoustic-based low-level feature classification methods is still rather far from satisfaction. The discrepancy between the limited descriptive power of low-level features and the richness of high-level semantics perceived by the human brain has become the 'bottleneck' problem in audio signal analysis. In this paper, functional magnetic resonance imaging (fMRI) which monitors the human brain's response under the natural stimulus of music/speech listening is used as high-level features in the brain imaging space (BIS). We developed a computational framework to model the relationships between BIS features and low-level features in the training dataset with fMRI scans, predict BIS features of testing dataset without fMRI scans, and use the predicted BIS features for music/speech classification in the application stage. Experimental results demonstrated the significantly improved performance of music/speech classification via predicted BIS features than that via the original low-level features. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2393347.2396322 | ACM Multimedia 2001 |
Keywords | Field | DocType |
fmri brain imaging,fmri scan,automatic music,human brain,bis feature,classification method,speech classification,audio track,audio signal analysis,low-level feature,high-level feature,current acoustic-based low-level feature,semantic gap | Computer vision,Audio signal,Functional magnetic resonance imaging,Computer science,Semantic gap,Active listening,Speech recognition,Speech classification,Artificial intelligence,Neuroimaging,Semantics | Conference |
Citations | PageRank | References |
6 | 0.45 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Jiang | 1 | 311 | 37.88 |
| Tuo Zhang | 2 | 233 | 32.92 |
| Xintao Hu | 3 | 118 | 13.53 |
| Lie LU | 4 | 1840 | 134.64 |
| Junwei Han | 5 | 3501 | 194.57 |
| Lei Guo | 6 | 1661 | 142.63 |
| Tianming Liu | 7 | 1033 | 112.95 |