A Generalized Audio Identification System Using Adaptive Filters

From searching music with smartphones to broadcast monitoring by radio channels, audio identification systems are being used more in recent years. Design of such systems may differ when the problem domain changes, since each environment has special conflicting constraints to consider, like required speed and robustness to signal degradations. In this paper, a widely used audio identification system originally developed by Haitsma and Kalker is analyzed from a signal processing point of view and the fingerprint (audio feature) extraction method is modified. By adding a flexible filter to the fingerprint extraction method, the original system can be tuned to work in different domains. In order to optimize the filters for each domain, Pareto optimization is used with the multi-obj ective genetic algorithm. The novel approach to make an adaptive audio identification system leads to the fact that instead of making fundamentally different systems for different domains, one general system can be developed and then modified to meet each domain-specific constraints. By using optimized filters, the average accuracy was increased to 94.28% from 69.78
^%
which is the average accuracy of the original system.