Due to the rapid development of digital music market, online music websites are widely available in our daily life. There is a practical need to develop automatic music genre classification algorithms to manage a huge amount of music. In this regard, the transitional information contained in pitches and beats should be very useful. Particularly, the transition in pitches produces a melody, and the transition in beats produces a rhythm. They both decide the music genre. To take these valuable information into consideration, we propose here a sequential naïve Bayes method for music genre classification. This method can be viewed as an novel extension of the classical naïve Bayes classifier, but takes the transitional information between pitches and beats into consideration. To reduce the number of estimated parameters, we propose a BIC-type criterion and develop a computationally efficient algorithm for model selection. The selection consistency of the BIC method is theoretically proved and numerically investigated. The finite sample performance of the proposed methods are assessed through both simulations and a real music dataset.

中文翻译：

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基于音高和节拍过渡信息的连续朴素贝叶斯方法用于音乐流派分类

由于数字音乐市场的快速发展，在线音乐网站在我们的日常生活中得到了广泛的应用。实际需要开发自动音乐体裁分类算法以管理大量音乐。在这方面，音调和节拍中包含的过渡信息应该非常有用。特别地，音高的过渡产生旋律，而拍子的过渡产生节奏。他们俩都决定了音乐流派。为了将这些有价值的信息考虑在内，我们在这里提出了一种连续的朴素贝叶斯方法来进行音乐流派分类。该方法可以看作是经典朴素贝叶斯分类器的一种新颖扩展，但它考虑了音高和节拍之间的过渡信息。为了减少估计参数的数量，我们提出了一种BIC型准则，并开发了一种用于模型选择的计算有效算法。从理论上证明了BIC方法的选择一致性，并进行了数值研究。通过仿真和真实音乐数据集评估了所提出方法的有限样本性能。