When the input sample feature size is one-third of the number of nodes in the first hidden layer, the network performance is basically convergent. The first hidden layer has the greatest impact on the prediction results. The results show that, when the DBN only contains one hidden layer and the number of neural nodes in the hidden layer is 117, the basic convergence accuracy is approximately 98%. On this basis, a music library classification retrieval learning platform has been established and tested. A national musical instrument recognition and classification network structure based on the DBN is proposed. In order to study the application of the deep learning (DL) method in music genre recognition, this study introduces the music feature extraction method and the deep belief network (DBN) in DL and proposes the parameter extraction feature and the recognition classification method of an ethnic music genre based on the DBN with five kinds of ethnic musical instruments as the experimental objects. It has strong ability and is appropriate for widespread implementation with the same number of iterations. This algorithm is 12 percent superior to the conventional algorithm, according to the research in this paper. The model’s parameters can be trained using conventional gradient descent techniques, and the model’s trained convolution neural network can learn the image’s features and finish the extraction and classification of the features. Feature extraction and neural networks are the tools employed in this paper.
It would be inefficient and unrealistic to attempt to classify music using manual labelling in the age of big data. Relying on manual labelling is how traditional music is classified. By examining users’ historical listening patterns for personalised recommendations, the music recommendation algorithm can lessen message fatigue for users and enhance user experience. People cannot easily search for the desired music without classifying enormous music resources and developing a successful music retrieval system.
From the cassette era to the CD era to the digital music era, the quantity of music has grown rapidly.