Modelling and characterization of an artificial neural network for infant cry recognition using mel-frequency cepstral coefficients

• Main Authors: Bandala, Argel A., Lim, Allimzon M., Cai, Mark Anthony D., Bacar, Allan Jeffrey C., Mañosca, Aynna Claudine G.
• Format: text
• Published: Animo Repository 2015
• Subjects: Auditory perception; Computational auditory scene analysis; Electrical and Electronics; Systems and Communications
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1359; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2358/type/native/viewcontent
• Institution: De La Salle University

This paper is about the creation of an artificial neural network (ANN) in MATLAB to analyze the features extracted from calculating the mel-frequency cepstral coefficients (MFCC) of the raw audio data. The paper explains basic concepts about the ANN, as well as the MFCC and other relevant theories. Regarding the design of the ANN, it uses multiple infant crying sounds, as well as non-crying sounds, to create a sample training set with a corresponding target that determines whether the sound is a cry or not. The paper uses relevant concepts heavily utilized in speech recognition for the design of the infant cry recognition, modifies them, and adds a few more calculations to fit the desired application to compensate for the differences present in a cry from human speech. © 2014 IEEE.