Instrumentation & Measurement Magazine 24-2 - 29

cepstrum of infant cry signal to capture its periodic patterns.
Then, the obtained cepstrum coefficients were fed to deep
feedforward neural network for training and classification under ten-fold cross-validation protocol.
Thanks to its ability to learn and model deep complex
structures in the data, the deep feedforward neural network
achieved very close to perfect accuracy when applied to expiration infant cry signals and yielded to perfect accuracy when
applied to inspiration infant cry signals. In addition, it outperformed the linear SVM and the Naïve Bayes systems when
tested both on the expiration and inspiration sets. Furthermore,
the proposed approach outperformed very recent works found
in the literature. In short, the proposed system for infant cry signal analysis and classification was found to be effective and fast.
In future work, some interesting issues will be considered.
First, regarding DFFNN, various topologies and transfer functions will be evaluated. As this is out of scope of the current
work, such investigation is expected to be comprehensive regarding the possible architectures, parameters, and transfer
functions. Second, other categories of deep learning neural
networks will be examined and compared to DFFNN, such
as convolutional neural network and long short-term memory networks. Third, we will examine the performance of
optimized classifiers when trained with two types of features,
namely deep features and nonlinear statistical features. Such
investigation will shed light on the effectiveness of other existing deep learning neural networks and on the discrimination
power of deep features compared to features used to describe
nonlinear dynamics in infant cry signals.

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April 2021	

Salim Lahmiri (salim.lahmiri@concordia.ca) is Assistant
Professor in the Supply Chain and Business Technology
Management Department at Concordia University, Montreal, Canada. He holds a Ph.D degree in computer science
continued on page 44

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