In this work, the generation of a deep learning model capable of predicting about
six types of modulation signals was proposed. 8PSK, B-FM, BPSK, DSB-AM, AFSK
and DPSK modulations were chosen. To obtain these signals, a system composed of a
RTL-SDR to capture real signals and a code in Matlab to generate synthetic signals was
used. The signals were generated in complex components, which were processed in order
to generate signals in the time domain and thus extract relevant characteristics to the
model. The mel-cepstral, pitch and spectral centroid coefficients are used to measure the
shape of signals. The architecture used is composed of convolution neural networks, these
divided into max-pooling and dropout layers. The result obtained for the model was an
accuracy of 98% for the validation set. The precision was 98% to 8PSK, 62% to AFSK,
97% to B-FM, 100% to BPSK, 100% to DSB-AM
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