  Solar radio bursts are some of the brightest emissions at radio frequencies
in the solar system. The emission mechanisms that generate these bursts offer a
remote insight into physical processes in solar coronal plasma, while fine
spectral features hint at its underlying turbulent nature. During radio noise
storms many hundreds of solar radio bursts can occur over the course of a few
hours. Identifying and classifying solar radio bursts is often done manually
although a number of automatic algorithms have been produced for this purpose.
The use of machine learning algorithms for image segmentation and
classification is well established and has shown promising results in the case
of identifying Type II and Type III solar radio bursts. Here we present the
results of a convolutional neural network applied to dynamic spectra of NenuFAR
solar observations. We highlight some initial success in segmenting radio
bursts from the background spectra and outline the steps necessary for burst
classification.
