Query Similar Music by Correlation Degree

  • Feng Yahzong
  • Zhuang Yueting
  • Pan Yunhe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2195)


We present in this paper a novel system for query by humming, our method differs from other ones in the followings: Firstly, we use recurrent neural network as the index of music database. Secondly, we present correlation degree to evaluate the music matching precision. We now hold a database of 201 pieces of music with various genres. The result of our experiment reports that the successful rate is 63% with top one matching and 87% with top three matching. Future work will be on melody extraction technique from popular formats of music and on-line music retrieval.


Recurrent Neural Network Correlation Degree Pitch Contour Pitch Period Acoustic Input 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Feng Yahzong
  • Zhuang Yueting
    • 1
  • Pan Yunhe
    • 1
  1. 1.Institute of Artificial IntelligenceZhejiang UniversityHangzhouP. R. China

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