Abstract
Searching for new features that contribute to the improvement of the performance of classification algorithms within the scientific area of infant crying classification for diagnostic purposes is a priority. Although several studies have suggested that some acoustic features present in the spectrogram of the signal of infant crying: stridor, melody, and shifts, could be interesting to reflect the pathological status of the newborn independently, a deeper study is still missing. This paper aims to demonstrate the potential of those attributes not sufficiently addressed in the state of the art of cry analysis when they’re properly combined. For this purpose, the Random Forest and k-Nearest Neighbor classification algorithms are used. The set of input vectors to the classifier also incorporates other well-known cry features that have proven to be effective in cry classification such as the Mel Frequency Cepstral Coefficients (MFCCs), fundamental frequency (F0), and the energy (E). The 10-fold cross-validation method was also used to evaluate the classifier performance as well as some standard metrics were used to evaluate the classifier results. Finally, a binary classifier for Central Nervous System (CNS) disorders with a Hypoxia background is proposed. The used experimental corpus comprises 616 samples of 1-s duration (253 pathological and 363 normal), corresponding to 54 children in age ranging from 0 to 3 months. The experimental results support the validity of the proposed feature set (stridor, melody, and shifts) for a child crying classification task as diagnostic method.
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Notes
- 1.
Two classes derived from six clinical control groups just for medical purpose.
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Acknowledgments
This work has been fully supported by the Belgian Development Cooperation through VLIR-UOS (Flemish Interuniversity Council-University Cooperation for Development) in the context of the Institutional University Cooperation programme with Universidad de Oriente.
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Cano-Ortiz, S.D., Martinez-Canete, Y., Veranes-Vicet, L. (2022). Evaluating New Set of Acoustical Features for Cry Signal Classification. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_14
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