Effect of Spectrogram Parameters and Noise Types on The Performance of Spectro-temporal Peaks Based Audio Search Method

Abstract

Audio search algorithms are used to detect the matching file in large databases, especially in multimedia applications and smart appliances. Based on different audio fingerprint extraction methods, similar algorithms have been developed and applied in different fields. These algorithms are expected to perform the detection in a reliable and robust way within the possible shortest time. In this study, based on spectral peaks method, an audio fingerprint algorithm with a few minor modifications was developed to accurately detect the matching audio file in the target database. The algorithm was demonstrated and then the effect of spectrogram parameters such as window size, overlap and number of FFT was investigated in terms of reliability and robustness of the program under three different noise sources. The database was a relatively small size one with five genres of music. In this study, it was aimed to contribute to new audio file detection studies based on spectral peaks method. It was observed that the the variation in the spectrogram parameters have significantly affected the number of matchings (NM), reliability and robustness. Under high noise conditions the optimal spectrogram parameters were determined as 512;50%;512, respectively. We did not observe, however, a significant effect of music genre on NM.