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PubMed Publications| Keyword search (4,163 papers available) |  |
"prosody" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm | Lahmiri S; Tadj C; Gargour C; | 41294952 ENCS |
| 2 | Grouping by Time and Pitch Facilitates Free but Not Cued Recall for Word Lists in Normally-Hearing Listeners | Sares AG; Gilbert AC; Zhang Y; Iordanov M; Lehmann A; Deroche MLD; | 37338981 PSYCHOLOGY |
| 3 | Social decision-making in Parkinson's disease | Caballero JA; Auclair Ouellet N; Phillips NA; Pell MD; | 35997248 PSYCHOLOGY |
| 4 | Near native-like stress pattern perception in English-French bilinguals as indexed by the mismatch negativity. | Gilbert AC, Honda CT, Phillips NA, Baum SR | 33333337 PSYCHOLOGY |
| Title: | Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm | ||||
| Authors: | Lahmiri S, Tadj C, Gargour C | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/41294952/ | ||||
| DOI: | 10.3390/e27111109 | ||||
| Publication: | Entropy (Basel, Switzerland) | ||||
| Keywords: | Bayesian optimization; Mel-frequency cepstral coefficients; auditory-inspired amplitude modulation; deep feedforward neural networks; newborn cry; prosody; random search optimization; | ||||
| PMID: | 41294952 | Category: | Date Added: | 2025-11-26 | |
| Dept Affiliation: |
ENCS
1 Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC H3G 1M8, Canada. 2 Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada. |
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Description: |
Voice analysis and classification for biomedical diagnosis purpose is receiving a growing attention to assist physicians in the decision-making process in clinical milieu. In this study, we develop and test deep feedforward neural networks (DFFNN) to distinguish between healthy and unhealthy newborns. The DFFNN are trained with acoustic features measured from newborn cries, including auditory-inspired amplitude modulation (AAM), Mel Frequency Cepstral Coefficients (MFCC), and prosody. The configuration of the DFFNN is optimized by using Bayesian optimization (BO) and random search (RS) algorithm. Under both optimization techniques, the experimental results show that the DFFNN yielded to the highest classification rate when trained with all acoustic features. Specifically, the DFFNN-BO and DFFNN-RS achieved 87.80% ± 0.23 and 86.12% ± 0.33 accuracy, respectively, under ten-fold cross-validation protocol. Both DFFNN-BO and DFFNN-RS outperformed existing approaches tested on the same database. |
