Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models
We make use of information inside infant’s cry signal in order to identify the infant’s psychological condition. Gaussian mixture models (GMMs) are applied to distinguish between healthy full-term and premature infants, and those with specific medical problems available in our cry database. Cry patt...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/983147 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563763620872192 |
|---|---|
| author | Hesam Farsaie Alaie Chakib Tadj |
| author_facet | Hesam Farsaie Alaie Chakib Tadj |
| author_sort | Hesam Farsaie Alaie |
| collection | DOAJ |
| description | We make use of information inside infant’s cry signal in order to identify the infant’s psychological condition. Gaussian mixture models (GMMs) are applied to distinguish between healthy full-term and premature infants, and those with specific medical problems available in our cry database. Cry pattern for each pathological condition is created by using adapted boosting mixture learning (BML) method to estimate mixture model parameters. In the first experiment, test results demonstrate that the introduced adapted BML method for learning of GMMs has a better performance than conventional EM-based reestimation algorithm as a reference system in multipathological classification task. This newborn cry-based diagnostic system (NCDS) extracted Mel-frequency cepstral coefficients (MFCCs) as a feature vector for cry patterns of newborn infants. In binary classification experiment, the system discriminated a test infant’s cry signal into one of two groups, namely, healthy and pathological based on MFCCs. The binary classifier achieved a true positive rate of 80.77% and a true negative rate of 86.96% which show the ability of the system to correctly identify healthy and diseased infants, respectively. |
| format | Article |
| id | doaj-art-3a8d1b8a49f6493d9905bda91646e3f7 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-3a8d1b8a49f6493d9905bda91646e3f72025-02-03T01:12:34ZengWileyModelling and Simulation in Engineering1687-55911687-56052012-01-01201210.1155/2012/983147983147Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture ModelsHesam Farsaie Alaie0Chakib Tadj1École de Technologie Supérieure, Université du Québec, 1100 rue Notre-Dame Ouest, Montréal, QC, H3C 1K3, CanadaÉcole de Technologie Supérieure, Université du Québec, 1100 rue Notre-Dame Ouest, Montréal, QC, H3C 1K3, CanadaWe make use of information inside infant’s cry signal in order to identify the infant’s psychological condition. Gaussian mixture models (GMMs) are applied to distinguish between healthy full-term and premature infants, and those with specific medical problems available in our cry database. Cry pattern for each pathological condition is created by using adapted boosting mixture learning (BML) method to estimate mixture model parameters. In the first experiment, test results demonstrate that the introduced adapted BML method for learning of GMMs has a better performance than conventional EM-based reestimation algorithm as a reference system in multipathological classification task. This newborn cry-based diagnostic system (NCDS) extracted Mel-frequency cepstral coefficients (MFCCs) as a feature vector for cry patterns of newborn infants. In binary classification experiment, the system discriminated a test infant’s cry signal into one of two groups, namely, healthy and pathological based on MFCCs. The binary classifier achieved a true positive rate of 80.77% and a true negative rate of 86.96% which show the ability of the system to correctly identify healthy and diseased infants, respectively.http://dx.doi.org/10.1155/2012/983147 |
| spellingShingle | Hesam Farsaie Alaie Chakib Tadj Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models Modelling and Simulation in Engineering |
| title | Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models |
| title_full | Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models |
| title_fullStr | Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models |
| title_full_unstemmed | Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models |
| title_short | Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models |
| title_sort | cry based classification of healthy and sick infants using adapted boosting mixture learning method for gaussian mixture models |
| url | http://dx.doi.org/10.1155/2012/983147 |
| work_keys_str_mv | AT hesamfarsaiealaie crybasedclassificationofhealthyandsickinfantsusingadaptedboostingmixturelearningmethodforgaussianmixturemodels AT chakibtadj crybasedclassificationofhealthyandsickinfantsusingadaptedboostingmixturelearningmethodforgaussianmixturemodels |