Very quick audio searching: introducing global pruning to the Time-Series Active Search

Abstract

Previously, we proposed a histogram-based quick signal search method called Time-Series Active Search (TAS). TAS is a method of searching through long audio or video recordings for a specified segment, based on signal similarity. TAS is fast; it can search through a 24-hour recording in 1 second after a query-independent preprocessing. However, an even faster method is required when we consider a huge amount of audio archives, for example a month’s worth of recordings. Thus, we propose a preprocessing method that significantly accelerates TAS. The core part of this method comprises a global histogram clustering of long signals and a pruning scheme using those clusters. Tests using broadcast recording indicate that the proposed algorithm achieves a search speed approximately 3 to 30 times faster than TAS. In these tests, the search results are exactly the same as with TAS.

Publication
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

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