Performance Evaluation of Different Window Functions for Audio Fingerprint Based Audio Search Algorithm

Window functions are structures that are frequently used to increase the quality of the transformation when processing the signal in the time-frequency domain, especially when creating the spectrogram of the signal. In calculations for frequency information, Fourier transforms of short-time samples of the signal are transformed into spectrogram data with the help of window functions, and then operations are performed by using these data. In this study, the performance of three different window functions, which are used in obtaining spectrogram data in the algorithm developed for audio fingerprint-based audio recognition, have been tested under different artificial noise conditions. The results of these three different window functions were compared graphically, and a truth table was obtained based on the analyzed database. As a result of the simulations carried out in MATLAB, it has been observed that the hanning window function gives better spectral results for different noise levels, and accordingly it produces better matching results compared to the other two window functions as reported in former studies on different applications. In addition, some improvements about window length have been added into algorithm to obtain better results.