Music classification method based on adaptive CNN and semi-supervised self-training model

Abstract

The invention belongs to the field of machine learning, recommendation systems and text classification, and particularly relates to a music classification method based on a self-adaptive CNN and a semi-supervised self-training model.The method comprises the following steps: firstly, analyzing the importance of fine classification of music to user selection; extracting music lyric features and emotion features associated with the item through a natural language processing technology, and then extracting and preliminarily classifying the features by using a neural network based on an attention mechanism; and finally, performing fine classification on each song according to a semi-supervised self-training method. According to the method, the self-adaptive CNN model and the semi-supervised self-training model are combined to finely divide the music data, so that the user can accurately search favorite music, the time of the user is saved, and the search accuracy is improved.

Description

technical field [0001] The invention belongs to the field of machine learning, recommendation system and text classification, in particular to a music classification method based on adaptive CNN and semi-supervised self-training model. Background technique [0002] With the rapid development of network technology, the scale of online music continues to expand, and the music available to users on the Internet has grown exponentially. For music, emotion is the most essential feature. However, most music does not have a clear classification of emotion, which leads to an insufficiently detailed classification of music. Moreover, the types of music that users like may include a variety of music styles, resulting in a relatively large scale of music to be classified. Large, music classification is difficult, making it difficult for users to find their favorite music. Therefore, in the process of classifying music, in addition to performing vector conversion on the music lyrics it...

AI Technical Summary

Problems solved by technology

At the same time, the supervised classification algorithm needs a large amount of labeled data. However, in the massive data, obtaining a large amount of labeled data requires a lot of labor, and there are many disadvantages in relying on artificial methods to divide music styles, such as low classification efficiency, music style Difficulty in judging ownership, etc.

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