Improving learning efficiency in multi-objective simulated annealing programming for sound environment classification
| Autor: | José Ranilla, Alberto Cocaña-Fernández, Luciano Sánchez, Héctor A. Sánchez-Hevia, Roberto Gil-Pita |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Hearing aid
Computer science business.industry medicine.medical_treatment 020206 networking & telecommunications 02 engineering and technology Energy consumption Machine learning computer.software_genre Simulated annealing 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Algorithm design Mel-frequency cepstrum Artificial intelligence business Classifier (UML) computer |
| Zdroj: | SAM |
| DOI: | 10.1109/sam.2016.7569699 |
| Popis: | In this work, a classifier that jointly optimises the expected total classification cost and the energy consumption is presented. A numerical study is provided, where different alternatives are implemented on a hearing aid. Our proposal is capable of automatically classifying the acoustic environment that surrounds the user and choosing the parameters of the amplification that are best adapted to the user's comfort, while attaining relevant improvements in both classification and learning-related energy consumptions with small to negligible loss in classification accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|