Music/speech classification using high-level features derived from fmri brain imaging

Xi Jiang, Tuo Zhang, Xintao Hu, Lie Lu, Junwei Han, Lei Guo, Tianming Liu

科研成果: 书/报告/会议事项章节会议稿件同行评审

12 引用 (Scopus)

摘要

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.

源语言英语
主期刊名MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia
825-828
页数4
DOI
出版状态已出版 - 2012
活动20th ACM International Conference on Multimedia, MM 2012 - Nara, 日本
期限: 29 10月 20122 11月 2012

出版系列

姓名MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia

会议

会议20th ACM International Conference on Multimedia, MM 2012
国家/地区日本
Nara
时期29/10/122/11/12

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