Clustering of time series using a hierarchical linear dynamical system

The auditory cortex in the brain does effortlessly a better job of extracting information from the acoustic world than our current generation of signal processing algorithms. Abstracting the principles of the auditory cortex, the proposed architecture is based on Kalman filters with hierarchically coupled state models that stabilize the input dynamics and provide a representation space. This approach extracts information from the input and self-organizes it in the higher layers leading to an algorithm capable of clustering time series in an unsupervised manner. An important characteristic of the methodology is that it is adaptive and self-organizing, i.e. previous exposure to the acoustic input is the only requirement for learning and recognition, so there is no need of selecting the number of clusters.