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

摘要

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.