Infant cry recognition system: A comparison of system performance based on Mel frequency and linear prediction cepstral coefficients

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Abdulaziz Y.
Ahmad S.M.S.
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This paper describes the architecture of an automatic infant cry recognition system which main task is to identify and differentiate between pain and non-pain cries belonging to infants. The recognition system is mainly based on feed forward neural network architecture which is trained with the scaled conjugate gradient algorithm. This paper presents an in depth comparison of system performance whereby two different sets of features, namely Mel Frequency Cepstral Coefficient (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) are extracted from the audio samples of infant's cries and are fed into the recognition module. The system accuracy reported in this study varies from 57% up to 76.2% under different parameter settings. The results demonstrated that in general, the infant cry recognition system performs better by using the MPCC feature sets. �2010 IEEE.
Automatic recognition of infant cry , Feed-forward neural network , Linear prediction cepstral coefficients , Mel-frequency cepstral coefficients , Conjugate gradient method , Extraction , Feedforward neural networks , Information retrieval , Knowledge management , Natural language processing systems , Speech recognition , Audio samples , Automatic recognition , Feature sets , Infant cry , Infant cry recognition , Linear prediction cepstral coefficients , Main tasks , Mel-frequency cepstral coefficients , Parameter setting , Performance based , Recognition systems , Scaled conjugate gradient algorithm , System accuracy , Forecasting