  We present a neural network architecture able to efficiently detect
modulation scheme in a portion of I/Q signals. This network is lighter by up to
two orders of magnitude than other state-of-the-art architectures working on
the same or similar tasks. Moreover, the number of parameters does not depend
on the signal duration, which allows processing stream of data, and results in
a signal-length invariant network. In addition, we have generated a dataset
based on the simulation of impairments that the propagation channel and the
demodulator can bring to recorded I/Q signals: random phase shifts, delays,
roll-off, sampling rates, and frequency offsets. We benefit from this dataset
to train our neural network to be invariant to impairments and quantify its
accuracy at disentangling between modulations under realistic real-life
conditions. Data and code to reproduce the results are made publicly available.
