Efficient music identification approach based on local spectrogram image descriptors

The diffusion of large music collections has determined the need for algorithms enabling fast song retrieval from query audio excerpts. This is the case of online media sharing platforms that may want to detect copirighted material. In this paper, we start from a proposed state-of-the-art algorithm for robust music matching based on spectrogram comparison leveraging computer vision concepts. We show that it is possible to further optimize this algorithm exploiting more recent image processing techniques and carrying out the analysis on limited temporal windows, still achieving accurate matching performance. The proposed solution is validated on a dataset of 800 songs, reporting an 80% decrease in computational complexity for an accuracy loss of about only 1%.