Quick searching of long audio signals using global pruning: accelerating time-series active search

摘要

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 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 signal and a pruning scheme using those clusters. Tests using broadcast recording indicate that the proposed algorithm achieves the search speed approximately 3 to 30 times faster than TAS. The exactly same search results as TAS are theoretically guaranteed.