Content-Based Classi cation and Retrieval of Audio

An online audio classiication and segmentation system is presented in this research, where audio recordings are classiied and segmented into speech, music, several types of environmental sounds and silence based on audio content analysis. This is the rst step of our continuing work towards a general content-based audio classiication and retrieval system. The extracted audio features include temporal curves of the energy function, the average zero-crossing rate, the fundamental frequency of audio signals, as well as statistical and morphological features of these curves. The classiication result is achieved through a threshold-based heuristic procedure. The audio database that we have built, details of feature extraction, classiication and segmentation procedures, and experimental results are described. It is shown that, with the proposed new system, audio recordings can be automatically segmented and classiied into basic types in real time with an accuracy of over 90%. Outlines of further classiication of audio into ner types and a query-by-example audio retrieval system on top of the coarse classiication are also introduced.