Generalized Time-Series Active Search With Kullback–Leibler Distance for Audio Fingerprinting

In this letter, a new audio fingerprinting approach is presented. We investigate to improve robustness by more precise statistical fingerprint modeling with common component Gaussian mixture models (CCGMMs) and Kullback–Leibler (KL) distance, which is more suitable to measure the dissimilarity between two probabilistic models. To address the resulting complexity, generalized time-series active search is proposed, which supports a wide variety of distance measures between two CCGMMs, including
$L_1$
,
$L_2$
, KL, etc. Experiments show that the new approach with KL distance increases robustness to distortions (including low-quality MP3 compression, small room echo, and play-and-record) while achieving efficient search.