Sound event detection of passing trains using convolutional recurrent neural network (CRNN)

Acoustic measurements near railway infrastructure typically involve precise identification and labelling of train passages in time series of noise measurements. The labelling of events is done manually by an acoustician, by cross-referencing several sources of information, such as train timetable data, audio data, images and/or videos. In this paper, we present a method which automatically detects and labels the passage of trains in front of a microphone using a convolutional recurrent neural network (CRNN). The advantage of this type of model is that it can be trained using existing data (time series of LAeq,1s and 1/3 octave band with a good labelling of passing trains); which has been widely available for a number of years. The results are promising if limited to binary classification but would require further development if the aim included the recognition and categorisation of various train types.