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Voice Quality Evaluation of Singing Art Based on 1DCNN Model

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  • Yang Liusong
  • Du Hui
  • Baiyuan Ding

Abstract

Traditional speech recognition still has the problems of poor robustness and low signal-to-noise ratio, which makes the accuracy of speech recognition not ideal. Combining the idea of one-dimensional convolutional neural network with objective evaluation, an improved CNN speech recognition method is proposed in this paper. The simulation experiment is carried out with MATLAB. The effectiveness and feasibility of this method are verified by simulation. This new method is based on one-dimensional convolutional neural network. The traditional 1DNN algorithm is optimized by using the fractional processing node theory, and the corresponding parameters are set. Establish an objective evaluation system based on improved 1DCNN. Through the comparison with other neural networks, the results show that the evaluation method based on the improved 1DCNN has high stability, and the error between subjective score and evaluation method is the smallest.

Suggested Citation

  • Yang Liusong & Du Hui & Baiyuan Ding, 2022. "Voice Quality Evaluation of Singing Art Based on 1DCNN Model," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, July.
    • Handle: RePEc:hin:jnlmpe:2074844
    DOI: 10.1155/2022/2074844
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