基于改进深度学习网络的音乐风格分类模型优化方法

Optimization Method of Music Style Classification Model Based on Improved Deep Learning Network

采用目前方法对音乐风格进行分类时,没有对提取的特征和进行融合处理,导致分类有效性差、时间复杂度高。对此基于改进深度学习网络提出一种音乐风格分类模型优化方法。在音高、节奏和音色三个方面对音乐进行特征提取,并在D-S证据理论的基础上对提取的特征进行融合处理,将融合后的音乐特征输入改进深度学习网络,构建音乐风格分类模型,实现音乐风格的分类。实验结果表明,所提方法的分类F1值高、时间复杂度低、ROC曲线趋近于1。

When the current method is used to classify music styles, the extracted features are not fused, resulting in poor classification effectiveness and high time complexity. An optimization method for music style classification model based on an improved deep learning network is proposed. Music features are extracted from three aspects of pitch, rhythm and timbre, and the extracted features are used on the basis of DS evidence theory. Music feature input is improved in the deep learning network to construct a music style classification model to realize the classification of music styles. The experimental results show that the classification F1 value of the proposed method is high, the time complexity is low, and the ROC curve approaches 1.