Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Analysis of correlation between window duration for kurtosis computation and accuracy of noise-induced hearing loss prediction
Ist Teil von
The Journal of the Acoustical Society of America, 2021-04, Vol.149 (4), p.2367-2376
Ort / Verlag
United States
Erscheinungsjahr
2021
Quelle
American Institute of Physics (AIP) Journals
Beschreibungen/Notizen
Kurtosis is considered an important metric for evaluating noise-induced hearing loss (NIHL). However, how to select window duration to calculate kurtosis remains unsolved. In this study, two algorithms were designed to investigate the correlation between window duration for kurtosis computation and the accuracy of NIHL prediction using a Chinese industrial database. Pure-tone hearing threshold levels (HTLs) and full-shift noise were recorded from each subject. In the statistical comparison, subjects were divided into high- and low-kurtosis groups based on kurtosis values computed over different window durations. Mann–Whitney U test was used to compare the difference in group HTLs to find the optimal window duration to best distinguish these two groups. In the support vector machine NIHL prediction model, kurtosis obtained from different window durations was used as a feature of the model for NIHL evaluation. The area under the curve was used to evaluate the performances of models. Fourteen window durations were tested for each algorithm. Results showed that 60 s was an optimal window duration that allows for both efficient computation and high accuracy for NIHL evaluation at test frequencies of 3, 4 and 6 kHz, and the geometric mean of kurtosis sequence was the best metric in NIHL evaluation.
Sprache
Englisch
Identifikatoren
ISSN: 0001-4966
eISSN: 1520-8524
DOI: 10.1121/10.0003954
Titel-ID: cdi_pubmed_primary_33940921
Format
–
Weiterführende Literatur
Empfehlungen zum selben Thema automatisch vorgeschlagen von bX