CLUSTERING OF TIME SERIES USING A HIERARCHICAL LINEAR DYNAMICAL SYSTEM

The auditory cortex in the brain does effortlessly a better job of ex-tracting information from the acoustic world than our current gener-ation of signal processing algorithms. Abstracting the principles of the auditory cortex, the proposed architecture is based on Kalman fil-ters 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 unsu-pervised 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. Index Terms — Music information retrieval, kalman filters, dy-namical systems, hierarchical systems, clustering, time serie