Percussion Big Data Mining and Modeling Method Based on Deep Neural Network Model

In order to improve the analysis effector percussion waveform, this paper studies the percussion big data mining and modeling method based on the deep neural network model. Aiming at the problem of the high sampling rate of Analog to Digital Converter (ADC) when the wideband frequency-hopping Linear Frequency Modulation (LFM) percussion waveform is sampled by Nyquist, this paper proposes a method of under sampling, and conducts a simple theoretical analysis. When the signal-to-noise ratio is 35dB, the frequency measurement error is close to 1MHz, which can meet the requirements of frequency measurement accuracy. When the signal-to-noise ratio is higher than 35dB, the frequency measurement error gradually decreases and eventually stabilizes, with a frequency measurement accuracy of around 30 kHz. Due to the low environmental interference in the sound wave recognition of percussion instruments and the close distance between the hardware equipment and the percussion instruments in this paper, the recognition results of the model in this paper have high accuracy Compared with existing methods, this article is more reliable in identifying percussion sound waves. From the data, it can be seen that the method proposed in this article has better performance in waveform recognition in impact big data mining models.

@article{Song2024,
  title     = {Percussion Big Data Mining and Modeling Method Based on Deep Neural Network Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xi Song},
  doi       = {10.14569/IJACSA.2024.01511100},
  url       = {https://doi.org/10.14569/IJACSA.2024.01511100}
}