Many audio applications perform perception-based time-frequency (TF) analysis by decomposing sounds into a set of functions with good TF localization (i.e. with a small essential support in the TF domain) using TF transforms and applying psychoacoustic models of auditory masking to the transform coefficients. To accurately predict masking interactions between coefficients, the TF properties of the model should match those of the transform. This involves having masking data for stimuli with good TF localization. However, little is known about TF masking for mathematically well-localized signals. Most existing masking studies used stimuli that are broad in time and/or frequency and few studies involved TF conditions. Consequently, the present study had two goals. The first was to collect TF masking data for well-localized stimuli in humans. Masker and target were 10-ms Gaussian-shaped sinusoids with a bandwidth of approximately one critical band. The overall pattern of results is qualitatively similar to existing data for long maskers. To facilitate implementation in audio processing algorithms, a dataset provides the measured TF masking function. The second goal was to assess the potential effect of auditory efferents on TF masking using a modeling approach. The temporal window model of masking was used to predict present and existing data in two configurations: (1) with standard model parameters (i.e. without efferents), (2) with cochlear gain reduction to simulate the activation of efferents. The ability of the model to predict the present data was quite good with the standard configuration but highly degraded with gain reduction. Conversely, the ability of the model to predict existing data for long maskers was better with than without gain reduction. Overall, the model predictions suggest that TF masking can be affected by efferent (or other) effects that reduce cochlear gain. Such effects were avoided in the experiment of this study by using maximally-compact stimuli.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5119819 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166937 | PLOS |
Sci Rep
January 2025
Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying features from electroencephalogram (EEG) signals. We employed robust local mean decomposition (R-LMD) to decompose EEG data from each channel, recorded over a four-second period, into five modes.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Large-scale and detailed analyses of activity in the United States (US) remain limited. In this work, we leveraged the comprehensive wearable, demographic, and survey data from the All of Us Research Program, the largest and most diverse population health study in the US to date, to apply and extend the previous global findings on activity inequality within the context of the US. We found that daily steps differed by sex at birth, age, body characteristics, geography, and built environment.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand.
Active transportation, such as cycling, improves mobility and general health. However, statistics reveal that in low- and middle-income countries, male and female cycling participation rates differ significantly. Existing literature highlights that women's willingness to use bicycles is significantly influenced by their perception of security.
View Article and Find Full Text PDFJ Cardiothorac Surg
January 2025
Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, P. R. China.
Background: Paragangliomas are rare neoplasms arising from extra-adrenal chromaffin cells, with mediastinal paragangliomas representing an exceptionally rare subset. This report details the surgical management of a complex mediastinal paraganglioma case, presenting with refractory hypertension and invasion of critical surrounding structures. A comprehensive review of the current literature is included to underscore existing cases, enhance clinical awareness, and share our insights and experience in the diagnosis and treatment of this challenging condition.
View Article and Find Full Text PDFBMC Musculoskelet Disord
January 2025
Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.
Background: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.
Methods: Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception to February 6, 2024, to identify literature on the use of machine learning to predict revision, secondary knee injury (e.g.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!