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Multi-Tone Piano Transcription Based on Visualization and Knowledge Recognition Algorithm

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  • Yingjing Fan
  • Leipo Liu

Abstract

Scientific computing visualization is also known as visualization. In an academic sense, visualization is a way of computing. It can convert data into intuitive geometric images, making it easier to understand. Visualization is the use of computer graphics and image processing techniques. It is a technical means to convert piles of seemingly disorganized data into graphics or images through special computer methods. Recording is the process of recording sound signals on a medium. Specifically, it is the process of converting sound into electrical signals through microphones and amplifiers and recording them with different materials and techniques. Piano tuning is the adjustment of the strings so that they reach a certain high pitch. This paper aims to study the analysis and evaluation of multi-tone piano transcriptions based on scientific computing visualization and knowledge recognition algorithms. It expects to improve the quality of piano recording with the support of scientific visualization technology and knowledge recognition algorithm. This paper briefly explains the big data, and on this basis, the visual data mining technology is mentioned, and the current situation in the visual data mining is explained. A matrix calculation of the characteristic matrix of piano timbres was carried out to achieve the resynthesis of the 25th harmony in piano timbres electronically. The results of the trials in this article demonstrate that when the Gaussian number changes to 5 during the recording process, the difference that exists of the acoustic voltage intensity and the noise level phase is clearly diminished. When the Gauss figure is 10, there is an inversion of the value of the sound pressure phase.

Suggested Citation

  • Yingjing Fan & Leipo Liu, 2022. "Multi-Tone Piano Transcription Based on Visualization and Knowledge Recognition Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, July.
  • Handle: RePEc:hin:jnlmpe:6244225
    DOI: 10.1155/2022/6244225
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