The style feature extraction of electronic music based on kernel limit learning machine Online publication date: Thu, 23-Jan-2025
by Minyuan Jiang
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 16, No. 6, 2024
Abstract: In order to solve the problems of low recall, low signal-to-noise ratio and low extraction accuracy of traditional methods, a style feature extraction method of electronic music based on kernel limit learning machine was proposed. Firstly, build an electronic music signal acquisition architecture, and process the acquired signal by framing, median smoothing, and three-point linear smoothing. Then, wavelet transform and LMS algorithm are used to build an adaptive filtering model for electronic music signal, and the smoothed signal is denoised. Finally, the model of electronic music style feature extraction based on kernel limit learning machine is built, and the signal denoising results are input into the trained model to get the results of electronic music style feature extraction. The experimental results show that the maximum recall is 98.3%, the maximum SNR is 57.6 dB, and the extraction accuracy is always above 95.1%.
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