Hierarchical Linear Dynamical Systems: A new model for clustering of time series
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. The proposed architecture, Hierarchical Linear Dynamical System (HLDS), 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. In this paper we further investigate the properties of HLDS, demonstrate its performance on music rather than isolated notes and propose the time domain implementation to overcome one of its current bottlenecks.