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A novel infant cry recognition system using auditory model‐based robust feature and GMM‐UBM
Author(s) -
Jiang Lin,
Yi Yumei,
Chen Defeng,
Tan Ping,
Liu Xingbao
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5405
Subject(s) - mixture model , classifier (uml) , computer science , speech recognition , pattern recognition (psychology) , artificial intelligence , infant crying , feature extraction , psychoacoustics , gaussian , robustness (evolution) , feature (linguistics) , mel frequency cepstrum , gaussian network model , perception , psychology , biochemistry , chemistry , physics , linguistics , philosophy , crying , quantum mechanics , psychiatry , neuroscience , gene
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.