Abstract
With people's pursuit of music art, a large number of singers began to analyze the trend of music in the future and create music works. Firstly, this study introduces the theory of music pop trend analysis, big data mining technology, and related algorithms. Then, the autoregressive integrated moving (ARIM), random forest, and long-term and short-term memory (LSTM) algorithms are used to establish the image analysis and prediction model, analyze the music data, and predict the music trend. The test results of the three models show that when the singer's songs are analyzed from three aspects: collection, download, and playback times, the LSTM model can predict well the playback times. However, the LSTM model also has some defects. For example, the model cannot accurately predict some songs with large data fluctuations. At the same time, there is no big data gap between the playback times predicted by the ARIM model image analysis and the actual playback times, showing the allowable error fluctuation range. A comprehensive analysis shows that compared with the ARIM algorithm and random forest algorithm, the LSTM algorithm can predict the music trend more accurately. The research results will help many singers create songs according to the current and future music trends and will also make traditional music creation more information-based and modern.
Highlights
As an entertainment product, pop music has attracted more attention
Using image analysis and prediction of the development trend of pop music, the collection of resources in the music library, and the integration of user behavior on different platforms, we can analyze user data and preferences, provide various pop music data sets, accurately analyze the specific attributes of music works, and accurately control dynamic pop music. e trend of user preferences determines the form of pop music [4]. ere is little research on the image prediction of pop music trend all over the world
E current research aims to predict the music trends using the long-term and short-term memory (LSTM) algorithm and big data technology images, and they help the singers create songs according to the current and future trends of pop music. e innovation of this study is to select the most appropriate and accurate algorithm model by the comparative analysis of the autoregressive integrated moving (ARIM) algorithm, random forest algorithm, and LSTM algorithm. e results show that compared with the traditional image prediction model, the LSTM algorithm has a better prediction accuracy
Summary
Pop music has attracted more attention. According to relevant research, China’s mobile music market developed rapidly from 2013 to 2018. The development of many types of popular music determines the main development direction of music in the future to a certain extent [1]. It reflects the influence of many social behaviors on pop music and the audience’s preference for related music [2, 3]. Ere is little research on the image prediction of pop music trend all over the world. E current research aims to predict the music trends using the LSTM (long-term and short-term memory) algorithm and big data technology images, and they help the singers create songs according to the current and future trends of pop music. E current research aims to predict the music trends using the LSTM (long-term and short-term memory) algorithm and big data technology images, and they help the singers create songs according to the current and future trends of pop music. e innovation of this study is to select the most appropriate and accurate algorithm model by the comparative analysis of the ARIM algorithm, random forest algorithm, and LSTM algorithm. e results show that compared with the traditional image prediction model, the LSTM algorithm has a better prediction accuracy
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