Child Cry Classification - An Analysis of Features and Models

被引：5

作者：

Kulkarni, Prathamesh ^{[1
]}

Umarani, Sarthak ^{[1
]}

Diwan, Vaishnavi ^{[1
]}

Korde, Vishakha ^{[1
]}

Rege, Priti P. ^{[1
]}

机构：

[1] Coll Engn Pune COEP, Pune, Maharashtra, India

来源：

2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT) | 2021年

关键词：

MFCC; GFCC; KNN; SVM; random forest; feature extraction; spectrogram; FEATURE-SELECTION;

D O I：

10.1109/I2CT51068.2021.9418129

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper presents a study on the classification of child cries based on various features extracted through speech and auditory processing. Certain spectral and descriptive features vary significantly in a child's cry intended for a specific purpose. Firstly, the model was trained using individual features. Later, the best features were selected and the model was again trained by combining these features. Logistic regression, SVM, KNN and Random Forest models were used for classification. A total of 457 samples were used for training/testing the models from the dataset Donate-a-cry corpus.

引用

页数：7

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