Wavelet-based automatic cry recognition system for detecting infants with hearing-loss from normal infants

The Journal of Engineering

Mahmoud Mansouri Jam¹  Hamed Sadjedi¹ 

[1] Shahed University;

关键词: discrete wavelet transforms;    medical signal processing;    paediatrics;    pattern recognition;    medical disorders;    wavelet based automatic cry recognition system;    hearing loss detection;    normal infant;    infant cry classification;    Mel filter bank discrete wavelet coefficients;    feature vector;    pattern recognition;    signal silence elimination;    signal filtering;    signal preemphasising;    signal segmentation;   

DOI  :  10.1049/joe.2013.0107

来源: DOAJ

【 摘 要 】

Infant cry is a multimodal and dynamic behaviour that it contains a lot of information. Goal of this investigation is recognition of two groups of infants by new acoustic feature that has not used in infant cry classification. The cry of deaf infants and normal hearing infants is studied. ‘Mel filter-bank discrete wavelet coefficients (MFDWCs)’ have been extracted as feature vector. Infant cry classification is a pattern recognition problem such as ‘automatic speech recognition’, which in signal processing stage the authors performed some pre-processing included silence elimination, filtering, pre-emphasising and, segmentation. After applying the discrete wavelet transform on the Mel scaled log filter bank energies of a cry signal frames, MFDWCs feature vector was extracted. The feature vector, MFDWCs, of each cry sample has large length, so they used principle components analysis to reduce in feature space dimension, after training of neural network as classifier, they achieved to 93.2% correction rate in cry recognition of test data set. This result shows better efficiency in comparison with previous familiarised approaches.

【 授权许可】

Unknown