Audio Style Conversion Based on AutoML and Big Data Analysis

Abstract

In the field of audio style conversion research, the application of AutoML and big data analysis has shown great potential. The study used AutoML and big data analysis methods to conduct deep learning on audio styles, especially in style transitions between flutes and violins. The results show that using iterative learning for audio style conversion training, the training curve tends to stabilize after 100 iterations, while the validation curve reaches stability after 175 iterations. In terms of efficiency analysis, the efficiency of the yellow curve and the green curve reached 1.05 and 1.34, respectively, with the latter being significantly more efficient. This study achieved significant results in audio style conversion through the application of AutoML and big data analysis, successfully improving conversion accuracy. This progress has practical application value in multiple fields, including music production and sound effect design.