Extraction and recognition of music melody features using a deep neural network

Abstract

The music melody can be used to distinguish the genre style of music and can also be used for retrieving music works. This paper used a deep learning algorithm, the convolutional neural network (CNN), to extract the features of musical melodies and recognize genres. Three-tuple samples were used as training samples in the training process. Orthogonal experiments were conducted on the number of music segments and the type of activation function in the algorithm in the simulation experiments. The CNN algorithm was compared with support vector machine (SVM) and traditional CNN algorithms. The results showed that there were obvious differences in the pitch and melody curves of different genres of music; the recognition performance was best when the number of music segments was six and the activation function was relu; the CNN algorithm trained by three-tuple samples had better recognition accuracy and spent less recognition time.