To find favorite songs among massive songs has become a difficult problem. The song recommendation algorithm makes personalized recommendations by analyzing user’s historical behavior, which can reduce user’s information fatigue and improve the user experience. This paper studies a personalized song recommendation algorithm based on vocal features. The specific work includes three parts. Firstly, the spectrum feature extraction and observe feature extraction of songs. The spectrum includes three types of features: time domain, frequency domain, and amplitude, which implicitly describe the rhythm, notes, and high-pitched or soothing properties of songs. Furthermore, automatic note recognition methods are explored as explicit classification features. The characteristic of this work is to use the comprehensive features of spectrum and musical notes as the classification basis. Secondly, based on song of convolutional neural network (CNN) classification, it sets different types of song classification. For the training of CNN, ELU, and ReLU, RMSProp and Adam were explored, and their performance and characteristics during training were compared. The classification methods were compared under the two configurations based on the spectrum as the classification basis and the comprehensive characteristic frequency of the spectrum and the note as the basis. Thirdly, a personalized song recommendation method was based on CNN classification. Also, the reasons why classification of CNN is not suitable for direct song recommendation are analyzed, and then, a recommendation method based on song fragment classification is proposed. A threshold model that can distinguish between pseudodiscrete and true-discrete is proposed to improve the accuracy of song classification.
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