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Audio classification based on adaptive partitioning

This paper presents an audio classification system that provides improved accuracy, robustness and flexibility over reported content-based audio classification methods. The system reads an input audio file, performs segmentation and classification of the composite sounds contained within the file and, for each sound clip, determines the most plausible matching class of audio in the database. Improvements in the accuracy of audio classification are largely due to the partitioning of the input audio file into homogeneous segments while the incorporation of new class detection offers greater flexibility of use.