Instrumentation & Measurement Magazine 24-2 - 28

Table 2 - Processing time (in seconds) of each
classifier under 10-fold cross-validation
Expiration (EXP) set

Inspiration (INS) set

653.14

73.57

SVM

3.18

1.46

PNN

19.57

14.30

DFFNN

29.30

31.63

NB

Fig. 5. Bar plots of obtained performance measures from 10-fold crossvalidation protocol. The DFFNN outperforms the linear SVM, NB, and PNN
classifiers in terms of accuracy, sensitivity, and specificity. Bar plots of
obtained performance measures from expiration set under 10-fold crossvalidation protocol. (a) Expiration set. (b) Inspiration set.

INS sets under 10-fold cross-validation protocol, the NB is the
fast one, followed by PNN, DFFNN and SVM, respectively, as
shown in Table 2.
The DFFNN outperforms all classifiers and requires less
than one minute to converge. Its superiority can be explained
by the ability to capture complexities inherent in the data structure. Specifically, as they have high level of abstraction, the
DFFNN can fully account for the complex relationships between cepstrums and their corresponding classes. Moreover,
the slight improvement in performance measures observed for
the INS set that yield to perfect accuracy by DFFNN could be
attributed to its capability to learn and model large variability
in cepstrums, which is clearly observed in Fig. 4.

In addition, it is worth mentioning that the NB classifier
is fast since it requires only a small training data set to estimate the probabilities. However, it performed the worst,
which is most likely linked to violation of the assumption of
independent predictors, for instance, cepstrum coefficients.
Furthermore, it is worth mentioning that the performance of
the linear SVM is moderate, possibly because its key parameters have not been optimized, and it is very slow compared
to NB and DFFNN due to the complexity of the optimization
process that underlies the SVM and the complexity of the data
under study. The accuracy of the PNN is the lowest, and this
could be explained by the fact that this kind of artificial neural
network employs a Gaussian transfer function to process the
inputs. In this regard, its performance depends on the distribution of the inputs and on the value of the smoothing parameter
used to determine the length of the probability density function (PDF) of the transfer function.
Finally, it is worth mentioning that the DFFNN outperformed most recent studies in distinguishing between healthy
and unhealthy infant cry signals in terms of accuracy, including Hidden Markov Models trained with segmented
cry signals (83.79%) [1], probabilistic neural network trained
with prevalence of fundamental frequency glide, resonance
frequencies dysregulation, and Mel-frequency cestrum coefficients (67.00% to 88.71%) [2], and linear SVM trained with
Mel-frequency cepstral coefficients, tilt, and rhythm features,
(67.80%) [3]. Therefore, the proposed CAD system for infant
cry signal classification based on DFFNN trained with cepstrum coefficients appears to be effective and promising.

Conclusion
Infant cry signal analysis is a non-invasive acoustic evaluation
that represents an important tool for physicians for pathology diagnosis. The purpose of the current work was to design
a CAD system to distinguish between healthy and unhealthy
infant cry signals. In this regard, we proposed to calculate the

Table 1 - Experimental results from random split of the data into 50% learning and 50% testing
Accuracy

Sensitivity

Specificity

Accuracy

Expiration set

Sensitivity

Specificity

Inspiration set

SVM

56.63%

59.79%

53.48%

54.62%

51.61%

57.63%

NB

57.32%

55.91%

58.73%

57.10%

55.48%

58.71%

PNN

53.83%

52.88%

54.78%

52.80%

44.73%

60.86%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

DFFNN
28	

IEEE Instrumentation & Measurement Magazine	

April 2021



Instrumentation & Measurement Magazine 24-2

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