Audio classification and content description

The rapid increase of information imposes new demands of content management. The goal of automatic audio classification and content description is to meet the rising need for efficient content management.

In this thesis, we have studied automatic audio classification and content description. As description of audio is a broad field that incorporates many techniques, an overview of the main directions in current research is given. However, a detailed study of automatic audio classification is conducted and a speech/music classifier is designed. To evaluate the performance of a classifier, a general test-bed in Matlab is implemented.

The classification algorithm for the speech/music classifier is a k-Nearest Neighbor, which is commonly used for the task. A variety of features are studied and their effectiveness is evaluated. Based on feature’s effectiveness, a robust speech/music classifier is designed and a classification accuracy of 98.2% for 5 seconds long analysis windows is achieved.

Author: Andersson, Tobias

Source: Lulea University of Technology

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