An earlier work of the authors introduced an adapted version of the Computational Geometry Algorithm (CGA) designed to analyse an audio stream and produce a unique coding-independent fingerprint. As the adaptability and the induced calculation load of the proposed algorithm form a key characteristic for multiple applications, our current investigation aims to measure its performance and stability in dynamic, real-time applications, i.e., in large audio library indexing and dynamic audio recognition. In addition, we investigate the fact that context similarity is also evident across fingerprints; hence a number of comparisons are used to explore the possible uses of this highly desirable algorithmic feature.
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