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Audio fingerprinting, also named as audio hashing, has been well-known as apowerful technique to perform audio identification and synchronization. Itbasically involves two major steps: fingerprint (voice pattern) design andmatching search. While the first step concerns the derivation of a robust andcompact audio signature, the second step usually requires knowledge aboutdatabase and quick-search algorithms. Though this technique offers a wide rangeof real-world applications, to the best of the authors' knowledge, acomprehensive survey of existing algorithms appeared more than eight years ago.Thus, in this paper, we present a more up-to-date review and, for emphasizingon the audio signal processing aspect, we focus our state-of-the-art survey onthe fingerprint design step for which various audio features and theirtractable statistical models are discussed.

A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design