Article: Modelling and accuracy analysis of music style and beat feature recognition using a biological genetic algorithm Journal: International Journal of Arts and Technology (IJART) 2026 Vol.16 No.7 pp.115 - 130 Abstract: Music style defines a musical work's overall characteristics, while beats carry emotional undertones, and dance consists of rhythmic movements. This study introduces a music style and beat recognition model based on a genetic algorithm, optimising feature extraction and model selection to improve recognition accuracy. Results demonstrate that the model classifies music into three styles and correspondingly reclassifies the beat dataset. Combined with the previous music style recognition results, the most suitable model is selected from multiple beat recognition models through adaptive selection of fitness function to realise the final beat recognition of the song to be tested. Experimental results show that the model achieves good performance on F-measure and other metrics, and can effectively identify beat characteristics across different music styles. Through error analysis, it identifies the failure case laws of blues, rock and other styles, and provides data support for the accurate adaptation of performance actions and music emotions. Inderscience Publishers - linking academia, business and industry through research

Title: Modelling and accuracy analysis of music style and beat feature recognition using a biological genetic algorithm

Authors: Yi Jiang; Kai Song

Addresses: Sichuan Preschool Educators College, Mianyang, 621000, Sichuan, China ' College of Music and Dance, Aba Teachers University, Aba, 623002, Sichuan, China

Abstract: Music style defines a musical work's overall characteristics, while beats carry emotional undertones, and dance consists of rhythmic movements. This study introduces a music style and beat recognition model based on a genetic algorithm, optimising feature extraction and model selection to improve recognition accuracy. Results demonstrate that the model classifies music into three styles and correspondingly reclassifies the beat dataset. Combined with the previous music style recognition results, the most suitable model is selected from multiple beat recognition models through adaptive selection of fitness function to realise the final beat recognition of the song to be tested. Experimental results show that the model achieves good performance on F-measure and other metrics, and can effectively identify beat characteristics across different music styles. Through error analysis, it identifies the failure case laws of blues, rock and other styles, and provides data support for the accurate adaptation of performance actions and music emotions.

Keywords: biological model; style recognition; beat detection; deep learning.

DOI: 10.1504/IJART.2026.154952

International Journal of Arts and Technology, 2026 Vol.16 No.7, pp.115 - 130

Received: 22 Jan 2026
Accepted: 19 Mar 2026
Published online: 20 Jul 2026 *

Title: Modelling and accuracy analysis of music style and beat feature recognition using a biological genetic algorithm

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