Influence of a wearer's voice on noise dosimeter measurements

In recent years, interest in personal noise exposure has expanded beyond a workplace safety measure to become an effective means of investigating physiological effects of the acoustic environment on an individual. This work investigates the effects of the wearer's voice as a possible dominant sound source on body-mounted noise dosimeters and develops methods to improve the application of dosimeter measurements in medium-level noise environments. Subjects experienced a controlled set of acoustic conditions while wearing a dosimeter. In each condition, sound pressure levels were recorded with and without the subject speaking controlled phrases. Three experimental variables were considered-room type, noise type, and noise level. All three variables had a statistically significant effect upon the contribution of speech to a dosimeter measurement; for example, noise level was shown to cause a change in speech contribution by as much as 5.5 dB between sequential levels. Based upon the analysis, a method of predicting the decibel contribution of a wearer's voice was developed. The results of this study can be used to estimate the effect of a wearer's voice on dosimeter measurements in medium-level noise environments

Intelligibility of medically related sentences in quiet, speech-shaped noise, and hospital noise

Noise in healthcare settings, such as hospitals, often exceeds levels recommended by health organizations. Although researchers and medical professionals have raised concerns about the effect of these noise levels on spoken communication, objective measures of behavioral intelligibility in hospital noise are lacking. Further, no studies of intelligibility in hospital noise used medically relevant terminology, which may differentially impact intelligibility compared to standard terminology in speech perception research and is essential for ensuring ecological validity. Here, intelligibility was measured using online testing for 69 young adult listeners in three listening conditions (i.e., quiet, speech-shaped noise, and hospital noise: 23 listeners per condition) for four sentence types. Three sentence types included medical terminology with varied lexical frequency and familiarity characteristics. A final sentence set included non-medically related sentences. Results showed that intelligibility was negatively impacted by both noise types with no significant difference between the hospital and speech-shaped noise. Medically related sentences were not less intelligible overall, but word recognition accuracy was significantly positively correlated with both lexical frequency and familiarity. These results support the need for continued research on how noise levels in healthcare settings in concert with less familiar medical terminology impact communications and ultimately health outcomes

Healing built-environment effects on health outcomes: environment–occupant–health framework

An investigation examined the structured scientific evidence on healthcare facilities (the healing built environment – HBE) and its impact on patients’ health outcomes under a holistic conceptual evaluative framework. The integrative review considered 127 papers (of which 59 were review papers). It found there was no adequate framework that could integrate existing research findings holistically. Such a holistic framework needs to demonstrate the cumulative and interactive effects of various HBE characteristics on patients’ health outcomes and wellbeing. An environment–occupant–health (E-O-H) framework is proposed, taking a holistic perspective to identify and evaluate different HBE characteristics. The E-O-H framework should support future research by (1) identifying the HBE characteristics that affect health outcomes; (2) defining appropriate future research designs; and (3) understanding the need for holistic analysis of the integrated effects of diverse HBE characteristics on health outcomes

Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Farm detection using low resolution satellite images is an important part of digital agriculture applications such as crop yield monitoring. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. In this paper, semantic segmentation of farm areas is addressed using low resolution satellite images. The segmentation is performed in two stages; First, local patches or Regions of Interest (ROI) that include farm areas are detected. Next, deep semantic segmentation strategies are employed to detect the farm pixels. For patch classification, two previously developed local patch classification strategies are employed; a two-step semi-supervised methodology using hand-crafted features and Support Vector Machine (SVM) modelling and transfer learning using the pretrained Convolutional Neural Networks (CNNs). For the latter, the high-level features learnt from the massive filter banks of deep Visual Geometry Group Network (VGG-16) are utilized. After classifying the image patches that contain farm areas, the DeepLabv3+ model is used for semantic segmentation of farm pixels. Four different pretrained networks, resnet18, resnet50, resnet101 and mobilenetv2, are used to transfer their learnt features for the new farm segmentation problem. The first step results show the superiority of the transfer learning compared to hand-crafted features for classification of patches. The second step results show that the model trained based on resnet50 achieved the highest semantic segmentation accuracy.acceptedVersionPeer reviewe

Caracterização da qualidade acústica de salas de aula para prática e ensino musical

Resumo O músico necessita perceber adequadamente o som nos recintos destinados ao estudo e prática musical, o que é possível quando estes locais estão acusticamente preparados e permitem o desenvolvimento e aprimoramento da percepção sonora musical. Neste trabalho três salas de estudo e três salas de aula coletiva, destinadas ao ensino e prática de Música de uma universidade, foram caracterizadas acusticamente através da opinião dos músicos usuários e de medições da sua resposta impulsiva. As salas descritas pelos músicos como secas tiveram, nas bandas de frequência de oitava de 500 a 1000 Hz, um Tempo de Reverberação em torno de 0,3 segundos, entre 14 e 22 dB de Clareza e entre 88% a 96% de Definição. As salas caracterizadas como reverberantes tiveram um tempo ao redor de 1,5 segundos, Clareza de 1 dB e Definição de 40%. A opinião dos músicos permitiu compreender as preferências da qualidade acústica das salas e as informações fornecidas pelos músicos se mostraram coerentes com os dados das medições

AB-10-018: The effects of noise from building mechanical systems with tonal components on human performance and perception (1322-RP)

This study investigated the effects of noise from building mechanical systems with tonal components on human task performance and perception. Six different noise conditions based on in-situ measurements were reproduced in an office-like setting; all were set to approximately the same sound level (47 dBA) but could have one particular tonal frequency (120 Hz, 235 Hz, or 595 Hz) at one of two tonal prominence ratios (5 or 9). Thirty participants were asked to complete typing, grammatical reasoning, and math tasks plus subjective questionnaires, while being exposed for approximately 1 hour to each noise condition. Results show that the noise conditions that had tonal prominence ratios of 9 were generally perceived to be more annoying than those of 5, although statistically significant differences in task performance were not found. Other findings are (1) that higher annoyance/distraction responses were significantly correlated with reduced typing task performance; (2) that the noise characteristics most closely correlated to higher annoyance/distraction responses in this study were higher ratings of loudness followed by roar, rumble, and tones; and (3) that perception of more low frequency rumble in particular was significantly linked to reduced performance on both the routine and cognitively demanding tasks