This work was developed aiming to employ Statistical techniques to the field of Music Emotion Recognition, a well-recognized area within the Signal Processing world, but hardly explored from the statistical point of view. Here, we opened several possibilities within the field, applying modern Bayesian Statistics techniques and developing efficient algorithms, focusing on the applicability of the results obtained. Although the motivation for this project was the development of a emotion-based music recommendation system, its main contribution is a highly adaptable multivariate model that can be useful interpreting any database where there is an interest in applying regularization in an efficient manner. Broadly speaking, we will explore what role a sound theoretical statistical analysis can play in the modeling of an algorithm that is able to understand a well-known database and what can be gained with this kind of approach.

翻译：这项工作旨在将统计技术应用于信号处理世界内公认的、但几乎无法从统计角度加以探讨的音乐情感认知领域。在这里,我们打开了实地的若干可能性,应用现代贝叶斯统计技术和开发高效算法,重点是所取得成果的适用性。虽然该项目的动机是开发基于情感的音乐建议系统,但其主要贡献是一个高度可变的多变模式,它可以有助于解释任何数据库,只要它们有兴趣有效地应用正规化。广而言之,我们将探讨健全的理论统计分析在模拟能够理解一个众所周知的数据库的算法方面可以发挥什么作用,以及这种方法能够取得什么成果。