Novel Nonlinearity Extracting Method of Diverse Music Signals Based on Chaotic Techniques for Musical Processing System

Abstract

Diverse musical styles are crucial ways for human beings to represent their emotions and interact with each other, whereas the essentials of musical signals are a time-lagged nonlinear dynamical system and their nonlinearity is difficult to analyze by conventional approaches. In this paper, the music is firstly framed depending on the subsections of its structure, then the Lyapunov exponent and the correlation dimension of the music signal are computationally analyzed, which reveals that the internal construction of the music signal is sophisticated with weak chaotic features. By retrieving the local characteristics of the music signal and extrapolating its holistic characteristics, the nonlinearity of the signal rendered by diverse musical styles also has a distinguishable difference. It is observed from the experiments that the maximum Lyapunov exponent of music characterized as “happy” and “relaxing” reaches 0.23, while the range of fluctuations in the correlation dimensions spans from 3.2 to 5.7. Furthermore, a discrepancy of 4.1 is noted in the correlation dimensions of music classified as “loud” and “uplifting,” indicative of the intricate nature of music signals' internal structures and the attenuation of chaotic characteristics. The M5 model exhibits an accuracy of 91.26% for classical music, representing a 2.9% enhancement over conventional methodologies. According to the aforementioned chaotic analysis, the originally designed nonlinearity extracting pattern for diverse music signals in the musical recognizing system demonstrates excellent performance.