Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Collin and Lavandier [J. Acoust. Soc. Am. 134, 1146-1159 (2013)] proposed a binaural model predicting speech intelligibility against envelope-modulated noises, evaluated in 24 acoustic conditions, involving similar masker types. The aim of the present study was to test the model robustness modeling 80 additional conditions, and evaluate the influence of its parameters using an approach inspired by a variance-based sensitivity analysis. First, the data from four experiments from the literature and one specifically designed for the present study were used to evaluate the prediction performance of the model, investigate potential interactions between its parameters, and define their values leading to the best predictions. A revision of the model allowed to account for binaural sluggishness. Finally, the optimized model was tested on an additional dataset not used to define its parameters. Overall, one hundred conditions split into six experiments were modeled. Correlation between data and predictions ranged from 0.85 to 0.96 across experiments, and mean absolute prediction errors were between 0.5 and 1.4 dB.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.heares.2020.107937 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!