Creation Characteristics of Music Piano Arrangement Based on Distributed Sensors

Music piano arrangement is a popular creation method among the public at present, which can play a role in the promotion of traditional culture and the combination of popular culture. However, the traditional method of analyzing the creative characteristics of musical piano arrangements has limitations, and it is not suitable for today’s public review era. In this paper, to address the limitations, distributed sensor technology is employed. It utilizes the Top-k query method, KNN (one of the most basic approaches in data mining classification technology is the proximity algorithm, often known as the K closest neighbor classification algorithm) algorithm, and fused balance algorithm to classify the creative features of musical piano arrangements. It collects 50 classic piano arrangements and studies its compositional style, melody, and tone. The data results showed that, for the style characteristics, each algorithm has a high recognition rate for passion songs, and the three errors are small; for the melody characteristics, because the KNN algorithm has a delay in determining the rising tempo, the overall melody is not grasped correctly, and the Top-k query method is not sensitive to the descending tempo, and the recognition rate is low; for the tonal characteristics, the Top-k query method has a larger range of low-key judgments, so that the number of identifications is larger. As for the energy consumption of the system, as the number of nodes increases, the energy consumption of the three algorithms also increases gradually. Among them, the Top-k query method has the largest energy consumption, exceeding 400 W at 180 nodes, and its energy consumption growth rate continues to increase. The fusion algorithm fluctuates greatly, while the KNN algorithm is stable. This shows that KNN algorithm is more stable in terms of system performance, and its characteristics can be better summarized in the creation and research of piano adaptation.