Music Classification Method Using Big Data Feature Extraction and Neural Networks

Abstract

From the cassette era to the CD era to the digital music era, the quantity of music has grown rapidly. People cannot easily search for the desired music without classifying enormous music resources and developing a successful music retrieval system. By examining users’ historical listening patterns for personalised recommendations, the music recommendation algorithm can lessen message fatigue for users and enhance user experience. Relying on manual labelling is how traditional music is classified. It would be inefficient and unrealistic to attempt to classify music using manual labelling in the age of big data. Feature extraction and neural networks are the tools employed 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. This algorithm is 12 percent superior to the conventional algorithm, according to the research in this paper. It has strong ability and is appropriate for widespread implementation with the same number of iterations.