Identifying optimal feature sets for acoustic signal classification in environmental noise measurements

Whatever the sound source to be evaluated, spurious events or unwanted sounds will always be present in environmental noise measurements. Spurious events are not characteristic of standard residual noise and must be removed prior to subsequent analyses. Currently, the removal step is deferred solely to the objective evaluation of the sound pattern and/or spectrogram by an operator. This results in the loss of many man-hours. Machine learning can be used to develop a tool capable of recognizing and removing spurious events in noise measurements. The tool must be able to account for various sounds, whether human-made or animal, and must be applicable to any environmental scenario. This is not a straightforward task, in fact if humans can easily distinguish between two sounds, such as a birds' chirps and a car passing by, based on prior experience, a machine may not be able to do so without apprenticeship. Therefore, a learning methodology must be constructed for the machine by establishing recognizable patterns. The aim of this paper is to identify the feature sets which allow the algorithm to differentiate spurious sounds in the best way. These features will represent the semantic value of the signal.