With the continuous development of information technology and the arrival of the era of big data, music appreciation has also entered the digital development. Big data essence is highlighted by comparison with traditional data management and processing technologies. Under different requirements, the required time processing range is different. Music appreciation is an essential and important part of music lessons, which can enrich people’s emotional experience, improve aesthetic ability, and cultivate noble sentiments. Data processing of music information resources will greatly facilitate the management, dissemination, and big data analysis and processing of music resources and improve the ability of music lovers to appreciate music. This paper aims to study the digital development of music in the environment of big data, making music appreciation more convenient and intelligent. This paper proposes an intelligent music recognition and appreciation model based on deep neural network (DNN) model. The use of DNN allows this study to have significant improvement over the traditional algorithm. This paper proposes an intelligent music recognition and appreciation model based on the DNN model and improves the traditional algorithm. The improved method in this paper refers to the Dropout method on the traditional DNN model. The DNN is trained on the database and tested on the data. The results show that, in the same database, the traditional DNN model is 114 and the RNN model is 120. The PPL of the improved DNN model in this paper is 98, i.e., the lowest value. The convergence speed is faster, which indicates that the model has stronger music recognition ability and it is more conducive to the digital development of music appreciation.
In order to improve the music analysis technology, this paper studies the music analysis technology combined with the spectrum analysis technology and builds an intelligent audio analysis model. In this paper, the nonlinear theoretical method is adopted, and the motion equation of the audio frequency is obtained through variable processing, so as to obtain the mean square fluctuation of the two sound wave models and then obtain the entanglement and compression. Moreover, this paper introduces the combined mode method to describe the interaction of the two laser fields and atomic matter and verifies that both the differential modes are decoupled from the interaction and only the sum mode participates in the interaction. The experiment verifies that the music audio analysis system based on spectrum analysis technology proposed in this paper can play an important role in music analysis.
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