Pitch-Related Identification of Instruments in Classical Music Recordings

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• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8983
• 期：1
• 页码：194-209
• 全文大小：2,103 KB
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• 作者单位：El偶bieta Kubera (10)
  Alicja A. Wieczorkowska (11)
  
  10. University of Life Sciences in Lublin, Akademicka 13, 20-950, Lublin, Poland
  11. Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland
  
• 丛书名：New Frontiers in Mining Complex Patterns
• ISBN：978-3-319-17876-9
• 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

Identification of particular voices in polyphonic and polytimbral music is a task often performed by musicians in their everyday life. However, the automation of this task is very challenging, because of high complexity of audio data. Usually additional information is supplied, and the results are far from satisfactory. In this paper, we focus on classical music recordings, without requiring the user to submit additional information. Our goal is to identify musical instruments playing in short audio frames of polyphonic recordings of classical music. Additionally, we extract pitches (or pitch ranges) which combined with instrument information can be used in score-following and audio alignment, see e.g. [9, 20], or in works towards automatic score extraction, which are a motivation behind this work. Also, since instrument timbre changes with pitch, separate classifiers are trained for various pitch ranges for each instrument. Four instruments are investigated, representing stringed and wind instruments. The influence of adding harmonic (pitch-based) features to the feature set on the results is also investigated. Random forests are applied as a classification tool, and the results are presented and discussed.