A new query-by-humming system based on the score level fusion of two classifiers

  • Authors:

  • Gi Pyo Nam;Kang Ryoung Park;Sung-Joo Park;Soek-Pil Lee;Moo-Young Kim

  • Affiliations:

  • Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea;Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea;Digital Media Research Center, Korea Electronics Technology Institute, Seoul, Republic of Korea;Digital Media Research Center, Korea Electronics Technology Institute, Seoul, Republic of Korea;Department of Information and Communication Engineering, Sejong University, Seoul, Republic of Korea

  • Venue:
  • International Journal of Communication Systems
  • Year:
  • 2012

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Abstract

With the widespread use of multimedia devices, such as MP3 players, the necessity of a content-based retrieval is increased, which can find the stored music even if a user does not know the title or singer of the music. Consequently, a query-by-humming (QBH) system is introduced, which provides functionality that a user can find a piece of music by humming. Although there have been many researches into QBH, there has been little done to combine more than two classifiers based on various fusion methods. Hence, we propose a new method of QBH based on the score level fusion of two classifiers. This research is novel in the following three ways as compared with previous works. First, the features of the humming data are extracted by using musical note estimation based on the spectro-temporal autocorrelation (STA). We normalize the extracted features by using the mean-shifting, median filtering, average filtering, and min–max scaling methods. Second, a pitch-based dynamic time warping (DTW) method is used as the first classifier. We use the linear scaling (LS) method with the quantized binary (QB) code of the pitch data as the second classifier. Third, through the combination of these two classifiers based on the score level by the MIN rule, the performance of QBH is much enhanced. Experimental results with the 2006 MIREX QBSH and 2009 MIR-QBSH corpus databases showed that the performance of the proposed fusion method was best compared with single classifier and other fusion methods. Copyright © 2010 John Wiley & Sons, Ltd.