Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music

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• 关键词：Signal processing ; Folk music analysis ; Computational ethnomusicology ; Performer classification ; Mood classification ; Ornamentation detection ; Ornamentation recognition ; COSFIRE
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9256
• 期：1
• 页码：558-569
• 全文大小：525 KB
• 参考文献：1.Taylor, E.: The AB guide to music. The Associate Board of the Royal Schools of Music (Publishing), London (1989)
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• 作者单位：Andreas Neocleous (15) (16)
  George Azzopardi (16) (17)
  Christos N. Schizas (15)
  Nicolai Petkov (16)
  
  15. Department of Computer Science, University of Cyprus, Nicosia, Cyprus
  16. Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands
  17. Intelligent Computer Systems, University of Malta, Msida, Malta
  
• 丛书名：Computer Analysis of Images and Patterns
• ISBN：978-3-319-23192-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Ornamentations in music play a significant role for the emotion whi1ch a performer or a composer aims to create. The automated identification of ornamentations enhances the understanding of music, which can be used as a feature for tasks such as performer identification or mood classification. Existing methods rely on a pre-processing step that performs note segmentation. We propose an alternative method by adapting the existing two-dimensional COSFIRE filter approach to one-dimension (1D) for the automatic identification of ornamentations in monophonic folk songs. We construct a set of 1D COSFIRE filters that are selective for the 12 notes of the Western music theory. The response of a 1D COSFIRE filter is computed as the geometric mean of the differences between the fundamental frequency values in a local neighbourhood and the preferred values at the corresponding positions. We apply the proposed 1D COSFIRE filters to the pitch tracks of a song at every position along the entire signal, which in turn give response values in the range [0,1]. The 1D COSFIRE filters that we propose are effective to recognize meaningful musical information which can be transformed into symbolic representations and used for further analysis. We demonstrate the effectiveness of the proposed methodology in a new data set that we introduce, which comprises five monophonic Cypriot folk tunes consisting of 428 ornamentations. The proposed method is effective for the detection and recognition of ornamentations in singing folk music.