Cochlear implant datalogging accurately characterizes children's 'auditory scenes'. (4th March 2021)
- Record Type:
- Journal Article
- Title:
- Cochlear implant datalogging accurately characterizes children's 'auditory scenes'. (4th March 2021)
- Main Title:
- Cochlear implant datalogging accurately characterizes children's 'auditory scenes'
- Authors:
- Ganek, Hillary
Forde-Dixon, Deja
Cushing, Sharon L.
Papsin, Blake C.
Gordon, Karen A. - Abstract:
- Abstract : Objectives: This study sought to determine if children's auditory environments are accurately captured by the automatic scene classification embedded in cochlear implant (CI) processors and to quantify the amount of electronic device use in these environments. Methods: Seven children with CIs, 36.71 ( SD = 11.94) months old, participated in this study. Three of the children were male and four were female. Eleven datalogs, containing outcomes from Cochlear's™ Nucleus ® 6 (Cochlear Corporation, Australia) CI scene classification algorithm, and seven day-long audio recordings collected with a Language ENvironment Analysis (LENA; LENA Research Foundation, USA) recorder were obtained for analysis. Results: Results from the scene classification algorithm were strongly correlated with categories determined through human coding ( ICC = .86, CI = [−0.2, 1], F (5, 5.1) = 5.9, P = 0.04) but some differences emerged. Scene classification identified more 'Quiet' ( t (8.2) = 4.1, P = 0.003) than human coders, while humans identified more 'Speech' ( t (10.6) = −2.4, P = 0.04). On average, 8% (SD = 5.8) of the children's day was spent in electronic sound, which was primarily produced by mobile devices (39.7%). Discussion : While CI scene classification software reflects children's natural auditory environments, it is important to consider how different scenes are defined when interpreting results. An electronic sounds category should be considered given how oftenAbstract : Objectives: This study sought to determine if children's auditory environments are accurately captured by the automatic scene classification embedded in cochlear implant (CI) processors and to quantify the amount of electronic device use in these environments. Methods: Seven children with CIs, 36.71 ( SD = 11.94) months old, participated in this study. Three of the children were male and four were female. Eleven datalogs, containing outcomes from Cochlear's™ Nucleus ® 6 (Cochlear Corporation, Australia) CI scene classification algorithm, and seven day-long audio recordings collected with a Language ENvironment Analysis (LENA; LENA Research Foundation, USA) recorder were obtained for analysis. Results: Results from the scene classification algorithm were strongly correlated with categories determined through human coding ( ICC = .86, CI = [−0.2, 1], F (5, 5.1) = 5.9, P = 0.04) but some differences emerged. Scene classification identified more 'Quiet' ( t (8.2) = 4.1, P = 0.003) than human coders, while humans identified more 'Speech' ( t (10.6) = −2.4, P = 0.04). On average, 8% (SD = 5.8) of the children's day was spent in electronic sound, which was primarily produced by mobile devices (39.7%). Discussion : While CI scene classification software reflects children's natural auditory environments, it is important to consider how different scenes are defined when interpreting results. An electronic sounds category should be considered given how often children are exposed to such sounds. … (more)
- Is Part Of:
- Cochlear implants international. Volume 22:Number 2(2021)
- Journal:
- Cochlear implants international
- Issue:
- Volume 22:Number 2(2021)
- Issue Display:
- Volume 22, Issue 2 (2021)
- Year:
- 2021
- Volume:
- 22
- Issue:
- 2
- Issue Sort Value:
- 2021-0022-0002-0000
- Page Start:
- 85
- Page End:
- 95
- Publication Date:
- 2021-03-04
- Subjects:
- Electronic Media -- Scene Classification -- Nucleus 6 Processor -- Auditory Environment -- Datalogging
Cochlear implants -- Periodicals
617.882 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1556-9152 ↗
http://www.ingenta.com/journals/browse/whurr/cii ↗
http://www.ingentaconnect.com/content/maney/cii ↗
http://www.tandfonline.com/loi/ycii20 ↗
http://maneypublishing.com/ ↗ - DOI:
- 10.1080/14670100.2020.1826137 ↗
- Languages:
- English
- ISSNs:
- 1467-0100
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3292.724200
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 22535.xml