Robust music identification based on low-order zernike moment in the compressed domain

Conference Paper · January 2010with8 Reads
DOI: 10.1145/1835449.1835592 · Source: DBLP
Conference: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, Geneva, Switzerland, July 19-23, 2010

    Abstract

    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.