Instrumentation & Measurement Magazine 24-2 - 25

Fig. 1. Flowchart of the proposed CAD system based on cepstrum analysis and DFFNN for infant cry signal classification.
The pre-processing step includes signal denoising, artifact removing, and segmentation to separate expiration and
inspiration episodes. The resulting classification made by DFFNN is evaluated by standard performance measures.

network are employed as baseline classifiers. In this regard,
we seek to show that the DFFNN is effective compared to the
aforementioned, and investigation of various combinations of
DFFNN architectures and related functions are out of scope
of the current work. The proposed CAD system for infant cry
analysis and classification will be applied to a large data set obtained from a Canadian hospital located in Montreal, Quebec
and two other hospitals located in Lebanon.

Cepstrum Analysis
Basically, the cepstrum is the inverse Fourier transform of the
logarithm of the signal spectrum. In other words, it is essentially a spectrum of the original signal spectrum. The most
compelling feature of cepstrum is that any periodic pattern in
the spectrum arises as a particular component of cepstrum [9].
In this regard, cepstrum analysis has been employed in various
biomedical signal processing applications, including epileptic seizure detection [9], assessing Parkinson's disease severity
[10], newborn cry diagnostics [3], classification of heart sounds
[11], and estimation of heartbeat rate [12]. Besides, cepstrum
analysis was successfully employed in mechanical fault diagnosis [13]-[15].
Technically speaking, the cepstrum C[n] is computed as the
inverse discrete Fourier transform (IDFT) of the log magnitude
of the DFT of a signal x[n], which is given as follows:
	







C n  IDFT log DFT x n 	(1)

Keep in mind that there is no formal guide on how to choose
the number of coefficients used to describe the cepstrum.

In this study, the number
of cepstrum coefficients
to estimate is set to 1000.
Indeed, we make the hypothesis that such number
can statistically describe
harmonics in the original
infant cry signal. Also, we
expect that the DFFNN
would take less time to converge when trained with a
feature vector of size 1000
in each single hidden layer.

Deep Feedforward Neural Network
The standard feedforward neural network (FFNN) is an artificial neural network with one hidden layer used to process
the inputs. Besides, the deep feedforward neural network
(DFFNN) has several hidden layers. Specifically, the information in DFFNN moves from the input layer through the hidden
layers to the output layer, and there is no feedback or loop in
the network [16] which basically makes it deep and fast. Recall
that deep learning artificial neural networks have been found
to be successful in environment sound classification [17], improving safety of elderly people [18], heart rate estimation [19],
environmental and biological measurement [20], and hand
gesture recognition [21].
The architecture of FFNN used for infant cry classification is presented in Fig. 2. Accordingly, there are one input
layer, three hidden layers, and one output layer. The number
of neurons is set to 1000 in the input layer and in each hidden
layer. The number of neurons in the output layer is set to one
to represent the class label: either a healthy cry record or an
unhealthy cry record. Each hidden neuron is used to process
output information of the input layer according to the following expression:
	

hX

 wX  b	(2)

where X is the input vector, w is the matrix of weights, and b is
the bias vector. In general, the neuron of the output is nonlinearly processed by a given activation function such as sigmoid,
tanh, ReLU, and ELU functions. For instance, the input h(X) is

Fig. 2. DFFNN with three hidden layers. The input layer has 1000 neurons, corresponding to the number of coefficients in the cepstrum. There are 1000 neurons
in each hidden layer. The network is fully connected. Specifically, every single neuron in a layer is connected to all neurons in the next layer. As a result, there is
one bias plus 1000*1000 connections in each hidden layer. The output layer has only one neuron that indicates the class label. In each hidden layer and in the
output layer, W indicates the corresponding weight matrix and the corresponding constant parameter b. The activation function used to process the output signal
from each layer is the sigmoid.
April 2021	

IEEE Instrumentation & Measurement Magazine	25



Instrumentation & Measurement Magazine 24-2

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