This article focuses on the prediction of the vibration frequency response of handheld probes. A novel approach that involves machine learning and readily available data from probes was explored. Vibration probes are efficient and affordable devices that provide information about testing airborne sound insulation in building acoustics. However, fixing a probe to a vibrating surface downshifts sensor resonances and underestimates levels. Therefore, the calibration response of the sensor included in a probe differs from the frequency response of that same probe. Simulation techniques of complex mechanical systems may describe this issue, but they include hardly obtainable parameters, ultimately restricting the model. Thus, this study discusses an alternative method, which comprises different parts. Firstly, the vibration frequency responses of 85 probes were measured and labelled according to six features. Then, Linear Regression, Decision Tree Regression and Artificial Neural Networks algorithms were analysed. It was revealed that decision tree regression is the more appropriate technique for this data. The best decision tree models, in terms of scores and model structure, were fine-tuned. Eventually, the final suggested model employs only four out of the six original features. A trade-off solution that involved a simple structure, an interpretable model and accurate predictions was accomplished. It showed a maximum average deviation from test measurements ranging from 0.6 dB in low- frequency to 3 dB in high-frequency while remaining at a low computational load. This research developed an original and reliable prediction tool that provides the vibration frequency response of handheld probes.

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.

During the last decade, soundscape research has become one of the most active topics in Acoustics. This work provides a nonlinear variable selection analysis over the well-known dataset ’emo-soundscapes’. Namely, we provide a selection of the soundscape indicators using a nonlinear and nonparametric model as a regression tree method. Modern techniques (proposed in the literature) have been used, first for ranking the variables and then for choosing the effective number of features. We have also compared and discussed our results with those provided previously in the literature. This study, based on modern techniques in selecting the effective number of variables, confirms the result presented in previous recent work (but based on a linear model) that very parsimonious models should be considered (in the case of a nonlinear model, it is based on very few variables, from 2 to 4, depending on the output). All the results are obtained by analyzing a single dataset. As future research works, we plan to extend our study by also considering alternative datasets.

Socioeconomic factors are increasingly considered in the study of forest fires. However, there is a gap in the literature on the possible relationship between basic services and infrastructures such as small rural schools and forest fires. Population decline in rural areas is leading to an increase in forest fire risk and social vulnerability to forest fires due to the abandonment of traditional agroforestry practices and the expansion of unmanaged forest canopy. In addition, rural schools are supposed to make rural municipalities livable and promote the people’s sense of community. In parallel, there is controversy over the closure of small local schools in sparsely populated rural areas worldwide. Our study identified that the forest area burned in the province of Avila (Central Spain), during the period 1996 to 2023, was higher in municipalities without rural primary schools. The presence of rural schools was as statistically significant as the influence of orographic variations of the territory, the number of incipient fires, and the reduction of population density during the same period. Our work contributes to highlighting the potential links between the decline of essential services in rural areas and the increase in forest fire risk, to urge policymakers to take a collaborative and holistic view.