What can you do with a bottle and a hanger? Students with high cognitive flexibility give more ideas in the presence of ambient noise

Creativity has become a favourable skill to develop in higher education due to its value in society. Ambient noise during creative performance has traditionally been regarded as an environmental stressor and distractor, but recent findings suggest a positive impact of ambient noise on creative performance. It is still unclear what drives these inconsistent findings and whether individual differences between students explain the differential impact of noise on their performance. This study investigated the impact of ambient noise on divergent thinking performance in undergraduates during the COVID-19 pandemic, when common learning spaces were restricted and people were instructed to work from home. It also explored how cognitive flexibility (e.g., the ability to switch between different tasks and explore different strategies to problems) interacted with the impact of noise. Forty-two undergraduates completed an adult computer-based version of the Dimensional Change Card Sort task (DCCS) (a measure of cognitive flexibility) in silence, and the Alternative Uses Task (a measure of divergent thinking) in silence and in ambient noise displayed through headphones. On average, participants gave more ideas in the presence of ambient noise than in silence, but these ideas were not more original. Furthermore, the impact of noise interacted with cognitive flexibility. Participants who were more efficient at the DCCS (suggesting better cognitive flexibility) gave more ideas in noise. These findings can help to inform educational institutes and students on the influence the physical environment might have on divergent thinking

Efectos de la contaminación acústica Vehicular en la salud de la población del distrito Tupac Amaru Inca, Pisco, 2021.

El ruido es una de principales factores cuyo exceso puede generar efectos nocivos en el ambiente y sobre todo en la salud de las personas, en ese sentido, la presente investigación tuvo como objetivo general determinar la relación entre la contaminación acústica vehicular y la salud de los pobladores del distrito de Tupac Amaru Inca 2021. Para ello se desarrolló la investigación bajo un enfoque cuantitativo, de tipo aplicada y con un diseño no experimental, descriptivo simple. Respecto a las técnicas se usó la recolección de datos y la aplicación de encuestas, mientras que en los instrumentos de investigación se usó el protocolo nacional de monitoreo de calidad de ruido y cuestionarios para estimar el impacto en la salud de los pobladores. Los resultados mostraron que el máximo valor obtenido en horario diurno fue de 79 dB y el mínimo de 64,8 dB, mientras que en horario nocturno el máximo valor 79,5 dB y el mínimo de 45,3 dB, asimismo, en horario diurno 14 puntos de monitoreo registraron valores que excedieron el estándar de calidad ambiental (70 dB), es decir más del 50% de total de puntos, asimismo, en horario nocturno 14 puntos de monitoreo registraron valores que excedieron el estándar de calidad ambiental (60 dB), es decir más del 50% de total de puntos. Por otro lado, las encuestas evidenciaron que la población presenta efectos como el estrés, agitación respiratoria, alteración auditiva y efectos cardiovasculares

Deep Learning-based Speech Enhancement for Real-life Applications

Speech enhancement is the process of improving speech quality and intelligibility by suppressing noise. Inspired by the outstanding performance of the deep learning approach for speech enhancement, this thesis aims to add to this research area through the following contributions. The thesis presents an experimental analysis of different deep neural networks for speech enhancement, to compare their performance and investigate factors and approaches that improve the performance. The outcomes of this analysis facilitate the development of better speech enhancement networks in this work. Moreover, this thesis proposes a new deep convolutional denoising autoencoderbased speech enhancement architecture, in which strided and dilated convolutions were applied to improve the performance while keeping network complexity to a minimum. Furthermore, a two-stage speech enhancement approach is proposed that reduces distortion, by performing a speech denoising first stage in the frequency domain, followed by a second speech reconstruction stage in the time domain. This approach was proven to reduce speech distortion, leading to better overall quality of the processed speech in comparison to state-of-the-art speech enhancement models. Finally, the work presents two deep neural network speech enhancement architectures for hearing aids and automatic speech recognition, as two real-world speech enhancement applications. A smart speech enhancement architecture was proposed for hearing aids, which is an integrated hearing aid and alert system. This architecture enhances both speech and important emergency noise, and only eliminates undesired noise. The results show that this idea is applicable to improve the performance of hearing aids. On the other hand, the architecture proposed for automatic speech recognition solves the mismatch issue between speech enhancement automatic speech recognition systems, leading to significant reduction in the word error rate of a baseline automatic speech recognition system, provided by Intelligent Voice for research purposes. In conclusion, the results presented in this thesis show promising performance for the proposed architectures for real time speech enhancement applications

Effect of Distinct Ambient Noise Types on Mobile Interaction

The adverse effect of ambient noise on humans has been extensively studied in fields like cognitive science, indicating a significant impact on cognitive performance, behaviour, and emotional state. Surprisingly, the effect of ambient noise has not been studied in the context of mobile interaction. As smartphones are ubiquitous by design, smartphone users are exposed to a wide variety of ambient noises while interacting with their devices. In this paper, we present a structured analysis of the effect of six distinct ambient noise types on typical smartphone usage tasks. The evaluated ambient noise types include variants of music, urban noise and speech. We analyse task completion time and errors, and find that different ambient noises affect users differently. For example, while speech and urban noise slow down text entry, being exposed to music reduces completion time in target acquisition tasks. Our study contributes to the growing research area on situational impairments, and we compare our results to previous work on the effect of cold-induced situational impairments. Our results can be used to support smartphone users through adaptive interfaces which respond to the ongoing context of the user