Title: Adaptive classification of electronic music signals based on multiple machine learning models

Authors: Shuqing Li

Addresses: Department of Music and Dance, Luo'he Vocational Technology College, Luo'he, 462002, China

Abstract: In order to overcome the problems of low PSNR, low accuracy, and long-time consumption in traditional methods, an adaptive classification method of electronic music signals based on multiple machine learning models is designed. Electronic music signals are collected by sensors and filtered by sparse coding. The filtered signal is processed by frame segmentation and windowing, and the time domain and frequency domain characteristics of the signal are extracted. The multi-machine learning model is built by Naive Bayes, support vector machine and decision tree. The signal filtering results are taken as the input vector of the model, and the adaptive classification results of electronic music signals are taken as the output vector of the model to realise the adaptive classification of signals. Experimental results show that the maximum PSNR of this method is 55.66 dB, the classification accuracy is always above 96%, and the average classification time is 867 ms.

Keywords: multiple machine learning models; electronic music signals; adaptive classification; signal filtering; Naive Bayes; support vector machine; decision tree.

DOI: 10.1504/IJRIS.2025.148025

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.4, pp.238 - 245

Received: 17 Apr 2023
Accepted: 23 May 2023
Published online: 15 Aug 2025 *

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