A machine learning approach based on hybridization of genetic programming and neural networks is used to derive mathematical models for music genre classification. We design three multi-label classifiers with different trade-offs between complexity and accuracy, which are able to identify the degree of belonging of music content to ten different music genres. Our approach is innovative as it entirely relies on simple analytical functions and a reduced number of features. Resulting classifiers have an extremely low computational complexity and are suitable to be easily integrated in low-cost embedded systems for real-time applications. The GTZAN dataset is used for model training and to evaluate the accuracy of the proposed classifiers. Despite of the reduced number of features used in our approach, the accuracy of our models is found to be similar to that of more complex music genre classification tools previously published in the literature.

一种基于遗传编程和神经网络混合的机器学习方法被用于导出音乐流派分类的数学模型。我们设计了三个在复杂性和准确性之间进行权衡的多标签分类器，它们能够识别音乐内容对十种不同音乐流派的归属程度。我们的方法具有创新性，因为它完全依赖于简单的分析功能和数量减少的功能。所得分类器的计算复杂度极低，适合于轻松集成到低成本嵌入式系统中以进行实时应用。GTZAN数据集用于模型训练和评估提出的分类器的准确性。尽管在我们的方法中使用的功能减少了，