  Phonocardiogram (PCG) signal analysis is a critical, widely-studied
technology to noninvasively analyze the heart's mechanical activity. Through
evaluating heart sounds, this technology has been chiefly leveraged as a
preliminary solution to automatically diagnose Cardiovascular diseases among
adults; however, prenatal tasks such as fetal gender identification have been
relatively less studied using fetal Phonocardiography (FPCG). In this work, we
apply common PCG signal processing techniques on the gender-tagged Shiraz
University Fetal Heart Sounds Database and study the applicability of
previously proposed features in classifying fetal gender using both Machine
Learning and Deep Learning models. Even though PCG data acquisition's
cost-effectiveness and feasibility make it a convenient method of Fetal Heart
Rate (FHR) monitoring, the contaminated nature of PCG signals with the noise of
various types makes it a challenging modality. To address this problem, we
experimented with both static and adaptive noise reduction techniques such as
Low-pass filtering, Denoising Autoencoders, and Source Separators. We apply a
wide range of previously proposed classifiers to our dataset and propose a
novel ensemble method of Fetal Gender Identification (FGI). Our method
substantially outperformed the baseline and reached up to 91% accuracy in
classifying fetal gender of unseen subjects.
