Digital music distribution is increasingly powered by automated mechanisms that continuously capture, sort and analyze large amounts of Web-based data. This paper deals with the management of songs audio features from a statistical point of view. In particular, it explores the data catching mechanisms enabled by Spotify Web API and suggests statistical tools for the analysis of these data. Special attention is devoted to songs popularity and a Beta model, including random effects, is proposed in order to give the first answer to questions like: which are the determinants of popularity? The identification of a model able to describe this relationship, the determination within the set of characteristics of those considered most important in making a song popular is a very interesting topic for those who aim to predict the success of new products.

数字音乐分发越来越多地由自动化机制提供支持，这些机制不断捕获、分类和分析大量基于 Web 的数据。本文从统计的角度处理歌曲音频特征的管理。特别是，它探索了 Spotify Web API 启用的数据捕获机制，并提出了用于分析这些数据的统计工具。特别关注歌曲的流行度，并提出了一个包含随机效应的 Beta 模型，以便首先回答以下问题：哪些是流行度的决定因素？对于那些旨在预测新产品成功的人来说，识别能够描述这种关系的模型，确定那些被认为对歌曲流行最重要的特征的集合是一个非常有趣的话题。