Improvement of Landmark-based Audio Fingerprinting

In this thesis, we will improve audio fingerprinting (AFP) systems. Audio fingerprinting is a powerful technology for music retrieval. AFP can be used to identify the most similar audio in the database based on the query. Users can record a query snippet in noisy situation, and input the query snippet to AFP system. AFP system will return the most similar audio. AFP systems can also be used to identify any “sound”, like speech, audio of movie snippets. The goal of this thesis is to improve the accuracy of a AFP system. We improve the step of landmarks extraction by considering the energy of landmarks, and assume that the peaks with higher energy are the more valuable features, thus combining them into landmarks with higher priority. In the final procedure, we calculate the landmark count with a given weight. With all methods, the system achieves 5.29% higher recognition rate than the original AFP system.