A novel infant cry recognition system using auditory model‐based robust feature and GMM‐UBM

Summary Recognizing infant cry is a meaning work, which can help new parent to understand infant's needs. Mostly, the motivation of existed recognition features is based on the psychoacoustic model. However, weak features are not enough to represent the details of infant cry. To address this is...

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Bibliographic Details
Published inConcurrency and computation Vol. 33; no. 11
Main Authors Jiang, Lin, Yi, Yumei, Chen, Defeng, Tan, Ping, Liu, Xingbao
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc 10.06.2021
Subjects
auditory model
Classifiers
Corrosion
Feature extraction
Feature recognition
GMM‐UBM
infant cry recognition
Model accuracy
Probabilistic models
Robustness
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Summary:Summary Recognizing infant cry is a meaning work, which can help new parent to understand infant's needs. Mostly, the motivation of existed recognition features is based on the psychoacoustic model. However, weak features are not enough to represent the details of infant cry. To address this issue, we propose a novel infant cry recognition system. In our system, the feature extraction method derived from auditory model, this model can address the auditory neural active representation. Additionally, we designed the intelligent system by a classifier named Gaussian Mixture Model‐Universal Background Model, which has a robust recognition performance to channel imbalance and corroded signal. The experiment results have shown a superior performance. Compared with the High‐order Spectral feature, a state‐of‐the‐art feature, when using Gaussian Mixture Model and General Background Model as classifier, the recognition accuracy improved by 14.6% and 13.7% under clear and corroded signal, respectively.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.5405