Objective: Acceptable noise level (ANL) determines the maximum noise level that a listener is willing to accept while listening to speech. The objective of this study was to determine the equivalence of ANL measured using different speech stimuli for native speakers who lived in the U.S. and Taiwan.
Design: ANLs were measured using English, Mandarin, and the international speech test signal (ISTS) at each site. The same babble noise was used across speech stimuli. The ANLs were considered equivalent if the difference was unlikely to be greater than 3 dB.
Study Sample: Thirty adults with normal hearing were recruited at each site.
Results: For each site, the equivalence test suggested that the native-language and foreign-language ANLs were equivalent. Between the two sites, ANLs measured using the listener's native language were also equivalent. Although the ISTS ANL obtained within each site was equivalent to, and highly correlated to, the native-language ANL, the data were unable to confirm the equivalence of the ISTS ANLs obtained from the two sites.
Conclusions: The results suggested the possibility of directly comparing ANL measures carried out in different countries using different languages. However, it remains unclear if the ISTS can serve as an international ANL stimulus.
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http://dx.doi.org/10.3109/14992027.2012.733422 | DOI Listing |
ACS Sens
January 2025
Department of Physics, Umeå University, Umeå SE-901 87, Sweden.
Bacterial spores are highly resilient and capable of surviving extreme conditions, making them a persistent threat in contexts such as disease transmission, food safety, and bioterrorism. Their ability to withstand conventional sterilization methods necessitates rapid and accurate detection techniques to effectively mitigate the risks they present. In this study, we introduce a surface-enhanced Raman spectroscopy (SERS) approach for detecting spores by targeting calcium dipicolinate acid (CaDPA), a biomarker uniquely associated with bacterial spores.
View Article and Find Full Text PDFAudiol Res
January 2025
Otolaryngology Unit, Department of Traslational Medicine and Neuroscience-DiBrain, University of Bari, 70124 Bari, Italy.
Aim: The aim of this study was to assess the subjective experiences of adults with different cochlear implant (CI) configurations-unilateral cochlear implant (UCI), bilateral cochlear implant (BCI), and bimodal stimulation (BM)-focusing on their perception of speech in quiet and noisy environments, music, environmental sounds, people's voices and tinnitus.
Methods: A cross-sectional survey of 130 adults who had undergone UCI, BCI, or BM was conducted. Participants completed a six-item online questionnaire, assessing difficulty levels and psychological impact across auditory domains, with responses measured on a 10-point scale.
Hum Brain Mapp
February 2025
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI.
View Article and Find Full Text PDFFront Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
View Article and Find Full Text PDFJAMIA Open
February 2025
Institute for Informatics, Data Science and Biostatistics, Washington University, Saint Louis, MO 63110, United States.
Objective: Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1).
Materials And Methods: EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode mapping schemes.
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