A comparison of extended fingerprint hashing and locality sensitive hashing for binary audio fingerprints

Hash tables have been proposed for the indexing of high-dimensional binary vectors, specifically for the identification of media by fingerprints. In this paper we develop a new model to predict the performance of a hash-based method (Fingerprint Hashing) under varying levels of noise. We show that by the adjustment of two parameters, robustness to a higher level of noise is achieved. We extend Fingerprint Hashing to a multi-table range search (Extended Fingerprint Hashing) and show this approach also increases robustness to noise. We then show the relationship between Extended Fingerprint Hashing and Locality Sensitive Hashing and investigate design choices for dealing with higher noise levels. If index size must be held constant, the Extended Fingerprint Hash is a superior method. We also show that to achieve similar performance at a given level of noise a Locality Sensitive Hash requires nearly a six-fold increase in index size which is likely to be impractical for many applications.