Improvement of subjective and objective sound quality evaluation accuracy with discrete firefly algorithm and back propagation neural network method

With the improvement of comfort requirements in noise environment, the research on vibration noise has developed from noise reduction to sound quality optimization. Under the premise of combining subjective and objective evaluation, the method of sound quality evaluation is constantly improved. This paper carries out the test design of subjective evaluation of sound quality, collects and screens sound samples, and conducts the subjective evaluation test of sound quality using the grade-based scorecard method. Subsequently, establish subjective and objective evaluation model of sound quality, and put forward Discrete Firefly Algorithm and Back Propagation neural network method (DFA-BP) sound quality evaluation model based on Back Propagation neural network method (BP) sound quality evaluation model. Compared with BP sound quality evaluation model, the relative error of prediction accuracy is within 10%, which provides a new method for further improving the subjective and objective evaluation model of sound quality.