A novel infant cry recognition system using auditory model‐based robust feature and GMM‐UBM. (20th June 2019)

• Record Type: Journal Article
• Authors: Jiang, Lin; Yi, Yumei; Chen, Defeng; Tan, Ping; Liu, Xingbao
• Other Names: Wang Zhibo guestEditor.; Jiang Lin guestEditor.; Suman Bilial guestEditor.; Wyrzykowski Roman guestEditor.; Szymanski Boleslaw K. guestEditor.; Bentes Cristiana Barbosa guestEditor.; França Felipe M.G. guestEditor.; Marzulo Leandro Augusto Justen guestEditor.; Mencagli Gabriele guestEditor.; Pilla Mauricio Lima guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-06-20
• Subjects: auditory model -- feature extraction -- GMM‐UBM -- infant cry recognition; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5405
• Languages: English
• ISSNs: 1532-0626

Abstract

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.