An information-theoretic metric of fingerprint effectiveness

Audio fingerprinting refers to the process of extracting a robust, compact representation of audio which can be used to uniquely identify an audio segment. Works in the audio fingerprinting literature generally report results using system-level metrics. Because these systems are usually very complex, the overall system-level performance depends on many different factors. So, while these metrics are useful in understanding how well the entire system performs, they are not very useful in knowing how good or bad the fingerprint design is. In this work, we propose a metric of fingerprint effectiveness that decouples the effect of other system components such as the search mechanism or the nature of the database. The metric is simple, easy to compute, and has a clear interpretation from an information theory perspective. We demonstrate that the metric correlates directly with system-level metrics in assessing fingerprint effectiveness, and we show how it can be used in practice to diagnose the weaknesses in a fingerprint design.