Infant cry classification by using different deep neural network models and hand-crafted features
Yazarlar (1)
Prof. Dr. Turgut ÖZSEVEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Biomedical Signal Processing and Control (Q1) | ||
| Dergi ISSN | 1746-8094 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 05-2023 |
| Cilt / Sayı / Sayfa | 83 / 1 / 1–12 | DOI | 10.1016/j.bspc.2023.104648 |
| Makale Linki | http://dx.doi.org/10.1016/j.bspc.2023.104648 | ||
| Özet |
| Crying is the way babies communicate with the outside world. These cries may be related to the needs of the baby or maybe an expression of a medical disorder. For this reason, infant cries are examined to support inexperienced parents and to make an early diagnosis if there is a medical disorder. Infant cry signals are classified using signal processing methods such as hand-crafted features or image processing methods based on the spectral image of the cry. In this study, we investigate the effect of using hand-crafted features and spectral images individually and hybrid in the classification of infant cries. In this context, experiments were conducted with the 1D CNN model, transfer learning, texture analysis methods, hand-crafted features, and their combination. In addition, the number of classes used in most of the studies in the literature is two or three, whereas in this study 5-classes in the dataset are used … |
| Anahtar Kelimeler |
| Deep neural network | Hand-crafted features | Image processing | Infant cry classification | Performance analysis | Texture analysis |
Science Direct
Infant cry classification by using different deep neural network models and hand-crafted features
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 46 |
| Scopus | 28 |