Feature Extraction and Analysis Method of Trombone Timbre Based on CNN Model

Abstract

In order to improve the accuracy of trombone timbre feature extraction, this paper combines the CNN model to construct a trombone timbre feature extraction model and summarizes the principle of trombone timbre signal. Moreover, this paper deduces the parameters of the trombone timbre signal and the corresponding network model and uses mathematical expressions to model the trombone timbre signal, which is convenient for theoretical analysis and processing of the trombone timbre signal. In addition, this paper provides a detailed discussion of time-frequency analysis techniques, including their advantages and limitations, which provide an algorithmic basis for working with trombone timbre signals. It can be seen that time-frequency analysis technology still has great advantages in trombone timbre signal processing. Finally, the simulation results show that the trombone timbre feature extraction method based on the CNN model proposed in this paper can effectively identify the trombone timbre in various musical performances.