An accurate infant cry classification system based on continuos hidden Markov model

• 主要な著者: Abdulaziz Y., Ahmad S.M.S.
• その他の著者: 57207857499
• フォーマット: Conference Paper
• 出版事項: 2023
• 主題: Continuos hidden Markov model; Infant pain cry classification; Linear prediction cepstral coefficints; Mel frequency cepstral coefficient; Extraction; Forecasting; Health; Information technology; Speech recognition; Audio samples; Baum-Welch algorithms; Classification system; Continuous hidden Markov model; Feasibility studies; Infant cry; Linear prediction; Linear prediction cepstral coefficients; Local feature vectors; Main tasks; Mel-frequency cepstral coefficients; Parameter setting; System accuracy; Hidden Markov models

This paper describes the feasibility study of applying a novel continuous Hidden Markov Model algorithm as a classifier to an automatic infant cry classification system which main task is to classify and differentiate between pain and non-pain cries belonging to infants. The classification system is...