Mobile Video Capture Targeted Narrowband Audio Content Classification

Audio content analysis and automatic content management research field is facing new challenges from personal home video material created with mobile camera phones. We developed an audio content analysis and segmentation system that is robust to low sampling rate used in mobile phone camcorder tools and operates also well for AMR compressed data. Audio signal is segmented in five different classes, which can be used to classify videos for mobile video content management applications. 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 an average recognition accuracy of 88.6% for the high quality audio, 88.6% for the narrowband audio and accuracy of 82.9% for an AMR compressed audio. The effect of sampling rate to the recognition results is not significant and the effect of the AMR compression is also relatively low