We present a theoretical analysis of the Philips audio fingerprinting method under Gaussian white noise distortion for correlated stationary Gaussian sources. Prior analyses were for white Gaussian sources, which do not model realistically real audio signals. Our approach relies on formulating the unquantized fingerprint as a quadratic form, which affords a systematic way to compute the model parameters. We provide closed-form analytical upper bounds for the probability of bit error of the hash, and we apply these expressions to real audio signals.

We present a novel theoretical analysis of the Philips audio fingerprinting method proposed by Haitsma, Kalker, and Oostveen (2001). Although this robust hashing algorithm exhibits very good performance, the method has only been partially analyzed in the literature. Hence, there is a clear need for a more complete analysis which allows both performance prediction and systematic optimization. We examine here the theoretical performance of the method for Gaussian inputs by means of a statistical model. Our analysis relies on formulating the unquantized fingerprint as a quadratic form, which affords a systematic way to compute the model parameters. We provide closed-form analytical upperbounds for the probability of bit error of the hash for two relevant scenarios: noise addition and desynchronization. We show that these results are useful when applied to real audio signals

Soft hashing, also known as robust hashing or perceptual hashing, consists of summarising multimedia data, so as to obtain a concise representation called a hash value. There has been an increasing interest in the soft hashing problem recently. Techniques implementing soft hashing intend to mirror the behaviour of cryptographic hashing, when the information to be hashed can be subject to different kinds of distortion. Many heuristic techniques for undertaking soft hashing of images and other multimedia data have been devised. Except for some attempts, a framework giving solid guidelines to solve the problem is largely lacking. We provide one possible approach to undertake the modelling of robust soft hashing, detailing the basic problems involved. We show how some prior schemes partly fit into our model.