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

Abstract

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 L1, L2, 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