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Computer Engineering and Networking pp 243–251Cite as

Application of Improved BP Neural Network in the Frequency Identification of Piano Tone

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 277))

Abstract

For the problems existing in the identification process of piano tone, this paper puts forward an MFCC-based (Mel Frequency Cepstrum Coefficient) feature extraction algorithm and a new piano tone identification method with BP neural network as the matching model. Using the MFCC feature extraction algorithm to extract parameters is a good alternative, which could improve the identification rate. Regarding the improved BP neural network as the matching model of tone identification consumes moderate training time and owns high recognition rate. Simulation results show that the piano tone identification combining the BP neural network with the MFCC algorithm is simple, fast, and highly accurate.

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Acknowledgement

This paper is supported by Scientific Research Fund of Hunan Provincial Education Department (11C0231, 12C0995).

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Authors and Affiliations

  1. Hunan Urban Construction College, Xiangtan, 411101, China

    Xu Chen & Jun Tang

Authors
  1. Xu Chen
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  2. Jun Tang
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Corresponding author

Correspondence to Xu Chen .

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Editors and Affiliations

  1. University of Texas at Dallas, Richardson, Texas, USA

    W. Eric Wong

  2. Chinese Academy of Sciences, Beijing, China, People’s Republic

    Tingshao Zhu

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© 2014 Springer International Publishing Switzerland

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Chen, X., Tang, J. (2014). Application of Improved BP Neural Network in the Frequency Identification of Piano Tone. In: Wong, W.E., Zhu, T. (eds) Computer Engineering and Networking. Lecture Notes in Electrical Engineering, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-319-01766-2_29

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  • DOI: https://doi.org/10.1007/978-3-319-01766-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01765-5

  • Online ISBN: 978-3-319-01766-2

  • eBook Packages: EngineeringEngineering (R0)

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