Digital media information compression through auditory content analysis

Abstract

Lots of repetitions exist in television programmes and other kinds of broadcastings. Besides traditional data compression, a new information compression method is proposed to detect repeated programme segments in order to further reduce information redundancy. This approach is different from former data compression methods in that it realizes compression in semantic level through extracting auditory contents rather than signal statistic information. This is the reason for which it is named auditory content-based information compression. In our method, LPC features are extracted at acoustic level, an LPC distance measure is defined to divide digital media stream into programme segments, and auditory content description is created by statistically weighting LPC coefficients for each programme segment. Applying the idea of LZW lossless data compression, a Content-based Information Retrieval algorithm is developed to complete information comparation, selection, combination, and updating of the segments, similarly as data compression and decompression operations. Finally depending on well accepted evaluating criterion, our experiments indicate an approving information compression result and confirm the completeness of the audio information redundance detection and the reversibility of the compression process.