The relationship between auditory temporal resolution and auditory object formation

2001 ◽  
Vol 110 (5) ◽  
pp. 2706-2706
Author(s):  
Camille C. Dunn ◽  
Thomas D. Carrell
Keyword(s):  
Temporal Resolution ◽  
Auditory Object ◽  
Auditory Temporal Resolution ◽  
The Relationship

Experience-dependent learning of auditory temporal resolution

Neuroreport ◽  
2014 ◽  
Vol 25 (2) ◽  
pp. 134-137 ◽  
Author(s):  
Srikanta K. Mishra ◽  
Manasa R. Panda
Keyword(s):  
Temporal Resolution ◽  
Auditory Temporal Resolution ◽  
Dependent Learning

Detection of Gap and Modulations: Auditory Temporal Resolution Deficits in Adults Who Stutter

2019 ◽  
Vol 72 (1) ◽  
pp. 13-21
Author(s):  
Dhatri S. Devaraju ◽  
Santosh Maruthy ◽  
Ajith U. Kumar
Keyword(s):  
Temporal Resolution ◽  
Adults Who Stutter ◽  
Auditory Temporal Resolution

scholarly journals Correcting Bias in Crowdsourced Data to Map Bicycle Ridership of All Bicyclists

Urban Science ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 62 ◽  
Author(s):  
Avipsa Roy ◽  
Trisalyn A. Nelson ◽  
A. Stewart Fotheringham ◽  
Meghan Winters
Keyword(s):  
Temporal Resolution ◽  
Test Data ◽  
Training Data ◽  
Residential Areas ◽  
Maricopa County ◽  
Covariate Data ◽  
Street Level ◽  
Crowdsourced Data ◽  
The Relationship ◽  
The City

Traditional methods of counting bicyclists are resource-intensive and generate data with sparse spatial and temporal detail. Previous research suggests big data from crowdsourced fitness apps offer a new source of bicycling data with high spatial and temporal resolution. However, crowdsourced bicycling data are biased as they oversample recreational riders. Our goals are to quantify geographical variables, which can help in correcting bias in crowdsourced, data and to develop a generalized method to correct bias in big crowdsourced data on bicycle ridership in different settings in order to generate maps for cities representative of all bicyclists at a street-level spatial resolution. We used street-level ridership data for 2016 from a crowdsourced fitness app (Strava), geographical covariate data, and official counts from 44 locations across Maricopa County, Arizona, USA (training data); and 60 locations from the city of Tempe, within Maricopa (test data). First, we quantified the relationship between Strava and official ridership data volumes. Second, we used a multi-step approach with variable selection using LASSO followed by Poisson regression to integrate geographical covariates, Strava, and training data to correct bias. Finally, we predicted bias-corrected average annual daily bicyclist counts for Tempe and evaluated the model’s accuracy using the test data. We found a correlation between the annual ridership data from Strava and official counts (R2 = 0.76) in Maricopa County for 2016. The significant variables for correcting bias were: The proportion of white population, median household income, traffic speed, distance to residential areas, and distance to green spaces. The model could correct bias in crowdsourced data from Strava in Tempe with 86% of road segments being predicted within a margin of ±100 average annual bicyclists. Our results indicate that it is possible to map ridership for cities at the street-level by correcting bias in crowdsourced bicycle ridership data, with access to adequate data from official count programs and geographical covariates at a comparable spatial and temporal resolution.

Response of Rice Ecological Indicators to Water Consumption Based on Multi-source Data in Irrigation District Scale

2020 ◽  
Author(s):  
Junming Yang ◽  
Yunjun Yao ◽  
Ke Shang ◽  
Xiaozheng Guo ◽  
Xiangyi Bei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Water Consumption ◽  
Growth And Development ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Fusion Method ◽  
Development Stages ◽  
The Relationship

<p>The study of law of crop water consumption in small scale such as irrigation area requires remote sensing image data with high spatial and temporal resolution, however, remote sensing images that possess both high temporal and spatial resolution cannot be obtained for technical reasons. To solve the problem, this paper present a multisource remote sensing data spatial and temporal reflectance fusion method based on fuzzy C clustering model (FCMSTRFM) and multisource Vegetation index (VI) data spatial and temporal fusion model (VISTFM), the Landsat8 OLI and MOD09GA data are combined to generate high spatial and temporal resolution reflectance data and the landsat8 OLI, MOD09GA and MOD13Q1 data are combined to generate high spatial and temporal resolution normalized vegetation index (NDVI) and enhanced vegetation index (EVI) data.</p><p>The rice area is mapped by spectral correlation similarity (SCS) between standard series EVI curve that based the EVI generated by VISTFM and average value of each EVI class that generated by classing Multiphase EVI into several class, the extraction results are verified by two methods: ground sample and Google Earth image. high spatial and temporal resolution Leaf area index (LAI) that covered the mainly rice growth and development stages is generated by higher precision method between artificial neural network and equation fitting that establish the relationship between NDVI, EVI and LAI. The yield of rice in the spatial scale is generated by establishing the relationship between yield and LAI of the mainly growth and development stages that has the maximum correlation with yield. Daily high spatial resolution evapotranspiration is generated by using multisource remote sensing data spatial and temporal reflectance fusion method to fusion the MODIS-like scale and Landsat-like scale evapotranspiration that generated by The Surface Energy Balance Algorithm for Land (SEBAL). Based on the data, the evapotranspiration, LAI and yield of rice, obtained by remote sensing methods, rice water growth function is established by Jensen, Blank, Stewart and Singh model.</p>

Auditory Brainstem Correlates of Perceptual Timing Deficits

2007 ◽  
Vol 19 (3) ◽  
pp. 376-385 ◽  
Author(s):  
Krista L. Johnson ◽  
Trent G. Nicol ◽  
Steven G. Zecker ◽  
Nina Kraus
Keyword(s):  
Speech Perception ◽  
Temporal Resolution ◽  
Biological Marker ◽  
Auditory Brainstem ◽  
Acoustic Cues ◽  
Backward Masking ◽  
Neural Encoding ◽  
Auditory Temporal Processing ◽  
Auditory Temporal Resolution ◽  
Sensory Processes

Children with language-based learning problems often exhibit pronounced speech perception difficulties. Specifically, these children have increased difficulty separating brief sounds occurring in rapid succession (temporal resolution). The purpose of this study was to better understand the consequences of auditory temporal resolution deficits from the perspective of the neural encoding of speech. The findings provide evidence that sensory processes relevant to cognition take place at much earlier levels than traditionally believed. Thresholds from a psychophysical backward masking task were used to divide children into groups with good and poor temporal resolution. Speech-evoked brainstem responses were analyzed across groups to measure the neural integrity of stimulus-time mechanisms. Results suggest that children with poor temporal resolution do not have an overall neural processing deficit, but rather a deficit specific to the encoding of certain acoustic cues in speech. Speech understanding relies on the ability to attach meaning to rapidly fluctuating changes of both the temporal and spectral information found in consonants and vowels. For this to happen properly, the auditory system must first accurately encode these time-varying acoustic cues. Speech perception difficulties that often co-occur in children with poor temporal resolution may originate as a neural encoding deficit in structures as early as the auditory brainstem. Thus, speech-evoked brainstem responses are a biological marker for auditory temporal processing ability.

Using mismatch negativity to measure auditory temporal resolution thresholds

Neuroreport ◽  
1999 ◽  
Vol 10 (10) ◽  
pp. 2079-2082 ◽  
Author(s):  
Renée N. Desjardins ◽  
Laurel J. Trainor ◽  
Stephanie J. Hevenor ◽  
Cindy P. Polak
Keyword(s):  
Temporal Resolution ◽  
Mismatch Negativity ◽  
Auditory Temporal Resolution

Auditory temporal resolution in birds: Discrimination of harmonic complexes

2002 ◽  
Vol 112 (2) ◽  
pp. 748-759 ◽  
Author(s):  
Robert J. Dooling ◽  
Marjorie R. Leek ◽  
Otto Gleich ◽  
Micheal L. Dent
Keyword(s):  
Temporal Resolution ◽  
Auditory Temporal Resolution
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