The music style classification technology can add style tags to music based on the content. When it comes to researching and implementing aspects like efficient organization, recruitment, and music resource recommendations, it is critical. Traditional music style classification methods use a wide range of acoustic characteristics. The design of characteristics necessitates musical knowledge and the characteristics of various classification tasks are not always consistent. The rapid development of neural networks and big data technology has provided a new way to better solve the problem of music-style classification. This paper proposes a novel method based on music extraction and deep neural networks to address the problem of low accuracy in traditional methods. The music style classification algorithm extracts two types of features as classification characteristics for music styles: timbre and melody features. Because the classification method based on a convolutional neural network ignores the audio’s timing. As a result, we proposed a music classification module based on the one-dimensional convolution of a recurring neuronal network, which we combined with single-dimensional convolution and a two-way, recurrent neural network. To better represent the music style properties, different weights are applied to the output. The GTZAN data set was also subjected to comparison and ablation experiments. The test results outperformed a number of other well-known methods, and the rating performance was competitive.
Statistical Neural Networks in the Classification of Alcoholic Liver Disease and Nonalcoholic Fatty Liver Disease
