FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4282961118> ?p ?o ?g. }

• W4282961118 abstract "The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge of the acoustic characteristics of COVID-19 cough sounds is limited but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds.By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the ComParE feature set, i.e., a standardized set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions.The experimental results demonstrate that a set of acoustic parameters of cough sounds, e.g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general automatic COVID-19 detection model performs significantly above chance level, i.e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201).Based on the acoustic correlates analysis on the ComParE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models." @default.
• W4282961118 created "2022-06-17" @default.
• W4282961118 creator A5003081855 @default.
• W4282961118 creator A5043060302 @default.
• W4282961118 creator A5049349192 @default.
• W4282961118 creator A5063262277 @default.
• W4282961118 creator A5074148655 @default.
• W4282961118 date "2022-06-01" @default.
• W4282961118 modified "2023-10-04" @default.
• W4282961118 title "The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection" @default.
• W4282961118 cites W1525461918 @default.
• W4282961118 cites W1969605785 @default.
• W4282961118 cites W1975347914 @default.
• W4282961118 cites W1999137901 @default.
• W4282961118 cites W2017048827 @default.
• W4282961118 cites W2034981711 @default.
• W4282961118 cites W2066833106 @default.
• W4282961118 cites W2119055664 @default.
• W4282961118 cites W2143426320 @default.
• W4282961118 cites W2146678746 @default.
• W4282961118 cites W2166561349 @default.
• W4282961118 cites W2168429113 @default.
• W4282961118 cites W2399474811 @default.
• W4282961118 cites W2559325020 @default.
• W4282961118 cites W2612603263 @default.
• W4282961118 cites W2618043980 @default.
• W4282961118 cites W2756108401 @default.
• W4282961118 cites W2886422188 @default.
• W4282961118 cites W2956824775 @default.
• W4282961118 cites W2989382520 @default.
• W4282961118 cites W3003472688 @default.
• W4282961118 cites W3005808401 @default.
• W4282961118 cites W3007406211 @default.
• W4282961118 cites W3010533881 @default.
• W4282961118 cites W3012751338 @default.
• W4282961118 cites W3014281460 @default.
• W4282961118 cites W3016448052 @default.
• W4282961118 cites W3016684513 @default.
• W4282961118 cites W3035207702 @default.
• W4282961118 cites W3046698956 @default.
• W4282961118 cites W3081734822 @default.
• W4282961118 cites W3082664757 @default.
• W4282961118 cites W3082914976 @default.
• W4282961118 cites W3086396783 @default.
• W4282961118 cites W3088067841 @default.
• W4282961118 cites W3089168043 @default.
• W4282961118 cites W3091000562 @default.
• W4282961118 cites W3091468319 @default.
• W4282961118 cites W3092136311 @default.
• W4282961118 cites W3092241701 @default.
• W4282961118 cites W3104924861 @default.
• W4282961118 cites W3104951425 @default.
• W4282961118 cites W3105837102 @default.
• W4282961118 cites W3107948185 @default.
• W4282961118 cites W3109783949 @default.
• W4282961118 cites W3118723804 @default.
• W4282961118 cites W3121230641 @default.
• W4282961118 cites W3128508004 @default.
• W4282961118 cites W3128950392 @default.
• W4282961118 cites W3137914271 @default.
• W4282961118 cites W3138591656 @default.
• W4282961118 cites W3140531618 @default.
• W4282961118 cites W3152531055 @default.
• W4282961118 cites W3158578478 @default.
• W4282961118 cites W3173449808 @default.
• W4282961118 cites W3193349642 @default.
• W4282961118 cites W4206458399 @default.
• W4282961118 doi "https://doi.org/10.1016/j.jvoice.2022.06.011" @default.
• W4282961118 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35835648" @default.
• W4282961118 hasPublicationYear "2022" @default.
• W4282961118 type Work @default.
• W4282961118 citedByCount "5" @default.
• W4282961118 countsByYear W42829611182022 @default.
• W4282961118 countsByYear W42829611182023 @default.
• W4282961118 crossrefType "journal-article" @default.
• W4282961118 hasAuthorship W4282961118A5003081855 @default.
• W4282961118 hasAuthorship W4282961118A5043060302 @default.
• W4282961118 hasAuthorship W4282961118A5049349192 @default.
• W4282961118 hasAuthorship W4282961118A5063262277 @default.
• W4282961118 hasAuthorship W4282961118A5074148655 @default.
• W4282961118 hasBestOaLocation W42829611181 @default.
• W4282961118 hasConcept C119857082 @default.
• W4282961118 hasConcept C138885662 @default.
• W4282961118 hasConcept C142724271 @default.
• W4282961118 hasConcept C151989614 @default.
• W4282961118 hasConcept C154945302 @default.
• W4282961118 hasConcept C15744967 @default.
• W4282961118 hasConcept C177264268 @default.
• W4282961118 hasConcept C177291462 @default.
• W4282961118 hasConcept C199360897 @default.
• W4282961118 hasConcept C2776401178 @default.
• W4282961118 hasConcept C2779134260 @default.
• W4282961118 hasConcept C28490314 @default.
• W4282961118 hasConcept C3008058167 @default.
• W4282961118 hasConcept C41008148 @default.
• W4282961118 hasConcept C41895202 @default.
• W4282961118 hasConcept C46312422 @default.
• W4282961118 hasConcept C524204448 @default.
• W4282961118 hasConcept C52622490 @default.
• W4282961118 hasConcept C71924100 @default.

• next