Search Results (4)

The COVID-19 pandemic’s rapid shift to working from home has fundamentally challenged residential acoustic design, which traditionally prioritises rest and relaxation rather than sustained concentration. However, a clear gap exists in understanding how acoustic needs and the subjective evaluation of soundscape appropriateness (SA) differ between these conflicting activities within the same domestic space. Addressing this gap, this study reveals critical differences in how people experience and evaluate home soundscapes during work versus relaxation activities in the same residential spaces. Through an online survey of 247 Chinese participants during lockdown, we assessed soundscape perception attributes, the perceived saliencies of various sound types, and soundscape appropriateness (SA) ratings while working and relaxing at home. Our findings demonstrate that working at home creates a more demanding acoustic context: participants perceived indoor soundscapes as significantly less comfortable and less full of content when working compared to relaxing (p < 0.001), with natural sounds becoming less noticeable (−13.3%) and distracting household sounds more prominent (+7.5%). Structural equation modelling revealed distinct influence mechanisms: while comfort significantly mediates SA enhancement in both activities, the effect is stronger during relaxation (R² = 0.18). Critically, outdoor man-made noise, building-service noise, and neighbour sounds all negatively impact SA during work, with neighbour sounds showing the largest detrimental effect (total effect size = −0.17), whereas only neighbour sounds and outdoor man-made noise significantly disrupt relaxation activities. Additionally, natural sounds act as a positive factor during relaxation. These results expose a fundamental mismatch: existing residential acoustic environments, designed primarily for rest, fail to support the cognitive demands of work activities. This study provides evidence-based insights for acoustic design interventions, emphasising the need for activity-specific soundscape considerations in residential spaces. As hybrid work arrangements become the norm post-pandemic, our findings highlight the urgency of reimagining residential acoustic design to accommodate both focused work and restorative relaxation within the same home. Full article

Noise is increasingly recognized as a factor impacting health, including its effects on online education. However, differences in the perception of acoustic environmental factors have been scarcely analyzed. This study aimed to evaluate perceived differences in the interference of five types of sound (traffic, voices, TV/radio/household appliances, music, and animals) while conducting autonomous and synchronous activities during online learning. It is also aimed to identify which activities are more affected by the domestic acoustic environment among a group of 4 synchronous and 6 autonomous activities. The data were obtained from an online survey distributed online among the students of the Universidad de las Américas in Quito, Ecuador. The differences between acoustical variables were evaluated using frequentist and inferential analysis. Findings indicated that traffic noise was the least disruptive sound for autonomous activities, likely due to reduced vehicle circulation during the COVID-19 lockdown. In contrast, voices were identified as the most disturbing noise source, underscoring that background speech can significantly disrupt concentration. Additionally, domestic noise is more disturbing while taking exams than during solving problem tasks, comprehensive reading, or group work, probably because during the exams students cannot control unwanted sound sources. These outcomes underscore the need for acoustic strategies in domestic educational settings to reduce noise-related distractions. Full article

The domestic environment plays a critical role in online education, particularly its acoustic aspect. This study investigates whether there are differences in the perception of acoustic variables between students who perceive their academic performance as negative (G1) and those who rate it positively (G2). It also explores differences between students who assess the quality of education as positive or negative, along with gender distinctions. Data were collected through an online survey conducted by Universidad de Las Américas, Quito, Ecuador, during the COVID-19 lockdown. Statistical analyses revealed significant differences in the perception of noise between groups G1 and G2: Students who rated their academic performance negatively experienced higher noise interference from sources like voices and household appliances. Similar patterns were observed when assessing educational quality. Gender differences also emerged, with women reporting higher levels of noise interference caused by sound sources (voices, TV/Radio/household appliances, and animals), and in the development of autonomous and synchronous tasks than men. The differences between the groups evaluated in pairs suggest that these aspects should be taken into account when designing domestic spaces and strategies to improve the quality of the acoustic environment for students. Full article

In recent years, great developments in online university education have been observed, favored by advances in ICT. There are numerous studies on the perception of academic performance in online classes, influenced by aspects of a very diverse nature; however, the acoustic environment of students at home, which can certainly affect the performance of academic activities, has barely been evaluated. This study assesses the influence of the home acoustic environment on students’ self-reported academic performance. This assessment is performed by calculating prediction models using the Recursive Feature Elimination method with 40 initial features and the following classifiers: Random Forest, Gradient Boosting, and Support Vector Machine. The optimal number of predictors and their relative importance were also evaluated. The performance of the models was assessed by metrics such as the accuracy and the area under the receiver operating characteristic curve (ROC_AUC-score). The model with the smallest optimal number of features (with 14 predictors, 9 of them about the perceived acoustic environment) and the best performance achieves an accuracy of 0.7794; furthermore, the maximum difference for the same algorithm between using 33 and 14 predictors is 0.03. Consequently, for simplicity and the ease of interpretation, models with a reduced number of variables are preferred. Full article