Previously, we proposed a histogram-based quick signal search method called Time-series Active Search (TAS). TAS has only been effective in the exact matching case, where the segments to be detected are assumed exactly same as the reference signal. Here, we extend the method so that it is applicable even if the features fluctuate. In addition to the feature modification, feature dithering is discussed to absorb feature fluctuations. Efficient time-scaled search is also investigated to cope with variations of the reference signal duration. Tests using broadcast recordings show that the extended method improves the accuracy in non-exact-matching tasks such as handclap detection and word spotting in a single-speaker's narration. The tests also show the speed-ups by pruning introduced in the time-scaled search.