Pattern recognition for sensor signals

In this paper we propose a universal strategy for the automatic interpretation of sensor signals. We focus on acoustic signals. However, any time series may be used. We assume that changes in an object´s state cause a typical and reproducible change in the characteristics of the acquired sensor signal. In such cases we can train pattern recognizers basing on Hidden-Markov-Models or support vector machines with data recordings of different object states and use these classifiers to assess the state of identical or similar objects. Our approach assumes that the sensor signals consist of elementary signal events and some kind of syntax defining their temporal relation (much like a musical score defines the temporal relation between notes). It is capable of automatically determining both, the elementary events and their syntax from the training data. We present experimental results from seven different applications from the fields of non-destructive testing, bio and music signal processing.