| Paper: | MMSP-P3.7 | ||
| Session: | Multimedia Database, Content Retrieval, Joint Processing and Standards | ||
| Time: | Wednesday, May 17, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Multimedia Signal Processing: Content-based information retrieval and pattern discovery | ||
| Title: | Mobile Video Capture Targeted Narrowband Audio Content Classification | ||
| Authors: | Satu-Marja Mäkelä, Johannes Peltola, Mikko Myllyniemi, VTT Electronics, Finland | ||
| Abstract: | Automatic content management research field is facing new challenges video material created with mobile camera phones. We developed audio content analysis and segmentation system that is robust to low sampling rate used in mobile phones and operates also well for AMR compressed data. Audio signal is segmented in five different classes. The classification was done with Bayesian Networks using topology of four-stage binary tree network that works in a hierarchical manner. Results were further smoothed within three second window using weighted sum of Bayesian networks class probabilities. The results show average recognition accuracy of 89.0% for high quality audio, 89.2% for narrowband audio and accuracy of 82.8% for AMR compressed audio. The effect of sampling rate to the recognition results is not significant and the effect of AMR compression is also relatively low. | ||
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