AbstractThe use of the quadcopter‐type drone is now at a mature and practical stage, and many major manufacturers are expanding their range of applications. Because of the high maneuverability and practicality of this flying object, how we ensure it is used for benign purposes in urban life has become a major issue. Most such drones are small enough to avoid many current airborne detection methods and cheap enough to be disposable. We were able to gather enough amount of multimedia sources including drone‐related sources using open online platforms, such as YouTube and other similar services. For a subset of commercial quadcopter drones, we collected sound sample data consisting of recordings and YouTube videos and built a trained simple nonlinear neural network filter to classify them. We used Mel‐frequency cepstral coefficients to reduce the amount of data as the input signal and created two‐dimensional spectrogram images to minimize the loss of temporal information in the sample, as suggested by previous research. Most of the acoustic signal energy in the drone sound existed in specific frequency bands and residual signal patterns were spread up to around 15 kHz. We designed and tested several recurrent and convolutional neural network models and one of the simple neural network models based on the LeNet architecture could classify over 10 different types of drones from acoustic data with over 97% accuracy including an additional path for the detection of general acoustic drone patterns.