Chronological Ordering of the Audio Data using 2D Spectrograms

The paper introduces an automatic quantitative method for both the speech fragments analysis and chronological ordering. The method consists of the following: audio fragments are initially presented in the form of two-dimensional spectrograms, then a large set of 1025 numerical descriptors extracting from both the raw spectrograms and their transforms is analyzed. The similarity value between two audio fragments is computed using a variation of the Weighted K-Nearest Neighbor scheme. A similarity tree is designed to visualize differences between the audio fragments. Some speech fragments of well-known politicians were used for the study. The proposed method proves to be efficient for chronological ordering of the audio fragments. It seems to introduce new ways of developing software systems for automated processing of audio archives and analysis of the speech characteristics.

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