Abstract
In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value.
Highlights
Music is a form of expression of culture
In order to improve the effect of music classification and detection, this paper proposes a music classification and detection algorithm under complex noise scene and tests its superiority and versatility
(1) An intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve some limitations existing in the current electronic music signal detection process
Summary
Music is a form of expression of culture. Under different musical instruments, rhythms, and arrangements, people can feel joy, anger, sorrow, and joy in music. Compared with other types of algorithms, music detection algorithms based on artificial neural networks and support vector machines are the most commonly used. They are all new technologies in the field of artificial intelligence [13,14,15]. In order to improve the effect of music classification and detection, this paper proposes a music classification and detection algorithm under complex noise scene and tests its superiority and versatility. Contributions of this paper are as follows: Complexity (1) An intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve some limitations existing in the current electronic music signal detection process. (4) Considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process (AHP) was used to calculate the weight of feature words in different positions of text titles and lyrics before, in, and after the text
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