In the last decade, soundscapes have become one of the most active topics in Acoustics, providing a holistic approach to the acoustic environment, which involves human perception and context. Soundscapes-elicited emotions are central and substantially subtle and unnoticed (compared to speech or music). Currently, soundscape emotion recognition is a very active topic in the literature. We provide an exhaustive variable selection study (i.e., a selection of the soundscapes indicators) to a well-known dataset (emo-soundscapes). We consider linear soundscape emotion models for two soundscapes descriptors: arousal and valence. Several ranking schemes and procedures for selecting the number of variables are applied. We have also performed an alternating optimization scheme for obtaining the best sequences keeping fixed a certain number of features. Furthermore, we have designed a novel technique based on Gibbs sampling, which provides a more complete and clear view of the relevance of each variable. Finally, we have also compared our results with the analysis obtained by the classical methods based on p-values. As a result of our study, we suggest two simple and parsimonious linear models of only 7 and 16 variables (within the 122 possible features) for the two outputs (arousal and valence), respectively. The suggested linear models provide very good and competitive performance, with $R^2>0.86$ and $R^2>0.63$ (values obtained after a cross-validation procedure), respectively.

翻译：在过去十年里,声景已成为声学中最活跃的话题之一,为听觉环境提供了一种涉及人类感知和背景的全方位方法。声景吸引的情绪是核心的,非常微妙和不为人注意(与语言或音乐相比)。目前,声景感知是文献中的一个非常积极的话题。我们向一个众所周知的数据集(即,声景指标的选择)提供了详尽的变量选择研究(即,声景指标的选择);我们考虑了两种声景描述器的线性声景情感模型:刺激和价值。应用了几种选择变数数量的排序方案和程序。我们还实施了一种交替优化方案,以获得最佳序列,固定一定的特征。此外,我们设计了一种基于Gibs抽样的新型技术,更完整和清晰地展示了每个变数的相关性。最后,我们还比较了我们的结果与基于P-价值的古典方法获得的分析结果:刺激值和价值;我们的研究的结果是,我们分别提出了两种简单和隐性直线模型,分别是7和16种(在可能的结果中),提供了两种简单和隐性直线模型。