Music retrieval involves searching for music that is played over loudspeakers in public places such as a coffee shop or shopping mall, or even on the street. However, in such environments, the music is accompanied by background noise such as people’s voices, vehicular noises, or the sound of machinery.
Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. MIR systems perform various functionalities such as music recommendation and music recognition. MIR applications such as Shazam, SoundHound, and Gracenote have already been developed for the iPhone, iPad, and other such mobile devices.
To develop a music retrieval system, first, it is necessary to create an audio fingerprint that can be matched against those stored in a music database. An audio fingerprint contains short summary information of an audio or a perceptual piece of audio content.
Then, to improve the retrieval rate, when a query is input in a noisy environment, first, it is necessary to find candidate audio matching the query from a lookup table (LUT). This increases the probability of correct music identification. In this study, we evaluate various pre-processing methods for a hash-based fingerprint system and determine the best one by determining the accuracy of searching for a query from an LUT.
Pre-processing can be carried out using various approaches such as normalization, noise reduction, and filtering. In this study, we evaluate the search accuracy of various such  methods by calculating the number of exact matches when searching from robust fingerprints