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Automatic Music Transcription: An Experiment with Simple Tools

  • Conference paper
Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7027))

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Abstract

This study is focused on an automatic music transcription, i.e. converting acoustic music, stored in the form of WAVE files, to MIDI like data. An experiment on this topic is the key point of the paper. The experiment is aimed on determining what kinds of music could be successfully recognized with a simple tool rather than on searching for new recognition methods. Fourier transform was used as the recognition tool. It was utilized for analysis of music of simple harmony played with basic musical instruments.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Plac Politechniki 1, 00-660, Warsaw, Poland

    Wladyslaw Homenda

  2. Faculty of Mathematics and Computer Science, University of Bialystok, ul. Sosnowa 64, 15-887, Bialystok, Poland

    Wladyslaw Homenda

Authors
  1. Wladyslaw Homenda
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Editor information

Editors and Affiliations

  1. College of Computer Science, Zhejiang University, 310027, Hangzhou, Zhejiang Province, P.R. China

    Yongchuan Tang

  2. Japan Advanced Institute of Science and Technology (JAIST), 923-1292, Tatsunokuchi, Ishikawa, Japan

    Van-Nam Huynh

  3. Artificial Intelligence Group, Engineering Mathematics Department, University of Bristol, BS8 1TR, UK

    Jonathan Lawry

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© 2011 Springer-Verlag Berlin Heidelberg

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Homenda, W. (2011). Automatic Music Transcription: An Experiment with Simple Tools. In: Tang, Y., Huynh, VN., Lawry, J. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2011. Lecture Notes in Computer Science(), vol 7027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24918-1_27

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  • DOI: https://doi.org/10.1007/978-3-642-24918-1_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24917-4

  • Online ISBN: 978-3-642-24918-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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