Robust music identification based on low-order zernike moment in the compressed domain
In this paper, we devise a novel robust music identification algorithm utilizing compressed-domain audio Zernike moment adapted from image processing techniques as the pivotal feature. Audio fingerprint derived from this feature exhibits strong robustness against various audio signal distortions including the challenging pitch shifting and time-scale modification. Experiments show that in our test dataset composed of 1822 popular songs, a 5s music query example which might have been severely corrupted is still sufficient to identify its original near-duplicate copy, with more than 90% top five precision rate.
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