| Titre : | Smartphone-Based System for Learning and Inferring Hearing Aid Settings (2016) |
| Auteurs : | Gabriel Aldaz ; Sunil Puria ; Larry J. Leifer |
| Type de document : | Article |
| Dans : | Journal of the American Academy of Audiology (JAAA) (Vol. 27, n°9, September 2016) |
| Article en page(s) : | p. 732-749 |
| Langues: | Anglais |
| Descripteurs : |
HE Vinci Prothèse auditive ; Téléphones portables |
| Résumé : |
Background:
Previous research has shown that hearing aid wearers can successfully self-train their instruments’ gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the “untrained system,” that is, the manufacturer’s algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The “trained system” first learned each individual’s preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). |
| Disponible en ligne : | Oui |
| En ligne : | https://research-ebsco-com.ezproxy.vinci.be/linkprocessor/plink?id=37e86e33-7ea7-36fc-b4d4-f531c876957c |
Exemplaires (1)
| Cote | Support | Localisation | Section | Disponibilité |
|---|---|---|---|---|
| Journal of the American Academy of Audiology (JAAA). Vol.27, n°9 (October 2016) | Périodique papier | Ixelles | Rez | Consultation sur place uniquement Exclu du prêt |
