To investigate how an Unmanned Air Vehicle (UAV) can detect manned aircraft with a single microphone, an audio data set is created in which UAV ego-sound and recorded aircraft sound are mixed together. A convolutional neural network is used to perform the air traffic detection. Due to restrictions on flying UAVs close to aircraft, the data set has to be artificially produced, so the UAV sound is captured separately from the aircraft sound. They are then mixed with UAV recordings, during which labels are given indicating whether the mixed recording contains aircraft audio or not. The model is a CNN which uses the features MFCC, spectrogram or Mel spectrogram as input. For each feature the effect of UAV/aircraft amplitude ratio, the type of labeling, the window length and the addition of third party aircraft sound database recordings is explored. The results show that the best performance is achieved using the Mel spectrogram feature. The performance increases when the UAV/aircraft amplitude ratio is decreased, when the time window is increased or when the data set is extended with aircraft audio recordings from a third party sound database. Although the currently presented approach has a number of false positives and false negatives that is still too high for real-world application, this study indicates multiple paths forward that can lead to an interesting performance. In addition, the data set is provided as open access, allowing the community to contribute to the improvement of the detection task.