Structural fingerprint based hierarchical filtering in song identification

  • Authors:

  • Qiang Wang; Gang Liu; Zhiyuan Guo; Jun Guo; Xiaoyu Chen

  • Affiliations:

  • Pattern Recognition & Intell. Syst. Lab., Beijing Univ. of Posts & Telecommun., Beijing, China;Pattern Recognition & Intell. Syst. Lab., Beijing Univ. of Posts & Telecommun., Beijing, China;Pattern Recognition & Intell. Syst. Lab., Beijing Univ. of Posts & Telecommun., Beijing, China;Pattern Recognition & Intell. Syst. Lab., Beijing Univ. of Posts & Telecommun., Beijing, China;Pattern Recognition & Intell. Syst. Lab., Beijing Univ. of Posts & Telecommun., Beijing, China

  • Venue:
  • ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
  • Year:
  • 2011

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Abstract

Automatic song identification has long been a research focus. In this paper, a novel structural fingerprint based hierarchical filtering method is proposed and it consists of two parts: one is the generation of fingerprint with both long structural information and low collision, and the other is an efficient searching algorithm based on a set of selective 2-level filters. Experiments conducted on a database of 10,000 songs show that our approach is fast enough and can achieve the accuracy of 99.7% on 5 second clips with the SNR at 0db comparable to the state-of-the-art.