Abstract
Human sound evaluations not only depend on the characteristics of the sound but are also driven by factors related to the listener and the situation. Our research aimed to investigate crucial factors influencing the perception of low-level sounds as they—in addition to the often-researched loud-level sounds—might be decisive to people’s quality of life and health. We conducted an online study in which 1,301 participants reported on up to three everyday situations in which they perceived low-level sounds, resulting in a total of 2,800 listening situations. Participants rated the sounds’ perceived loudness, timbre, and tonality. Additionally, they described the listening situations employing situational eight dimensions and reported their affective states. All sounds were then assigned to the categories natural, human, and technical. Linear models suggest a significant difference of annoyance ratings across sound categories for binary loudness levels. The ability to mentally fade-out sound was the most crucial situational variable after valence, arousal, and the situation dimensions positivity and negativity. We ultimately selected the most important factors from a large number of independent variables by applying the percentile least absolute shrinkage and selection operator (Lasso) regularization method. The resulting linear regression showed that this novel machine-learning variable-selection technique is applicable in hypothesis testing of noise effects and soundscape research. The typical problems of overfitting and multicollinearity that occur when many situational and personal variables are involved were overcome. This study provides an extensive database of evaluated everyday sounds and listening situations, offering an enormous test power. Our machine learning approach, whose application leads to comprehensive models for the prediction of sound perception, is available for future study designs aiming to model sound perception and evaluation.
Highlights
Myriad research has shown that annoyance reactions to unpleasant sounds can cause psychological stress (Gunn et al, 1975; Wolsink et al, 1993; Lercher, 1996; Stallen, 1999) that affects cognition and health (Serrou, 1995; Babisch, 2002; World Health Organization, 2011; Beutel et al, 2016; Klatte et al, 2017)
We demonstrated that linear mixed-effects models combined with novel machine learning variable-selection techniques are applicable in hypothesis testing in noise effects and soundscape research
Despite the limitations mentioned above, our study shows how to deal with many influencing variables in the field of sound perception using machine learning for the selection of the most essential variables
Summary
Myriad research has shown that annoyance reactions to unpleasant sounds can cause psychological stress (Gunn et al, 1975; Wolsink et al, 1993; Lercher, 1996; Stallen, 1999) that affects cognition and health (Serrou, 1995; Babisch, 2002; World Health Organization, 2011; Beutel et al, 2016; Klatte et al, 2017). To address these research aims, we conducted an online study wherein 1,301 participants reported on up to three everyday situations (including 32 relevant sound-related, situational, and person-related variables) in which they perceived low-level sounds. To handle this large number of variables, we implemented the percentile least absolute shrinkage and selection operator (Lasso) method, a linear machine learning approach, to select the crucial variables associated with annoyance ratings and to establish comprehensive models which overcome problems associated with overfitting and can predict annoyance for new data that were not involved in the model training and validation
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