Hidden Markov model search algorithm for non-convex duration distributions
Zuoying Wang · Journal of Tsinghua University(Science and Technology) · 2005
A duration distribution-based hidden Markov model (DDBHMM) was developed to replace the exponential duration distribution limitations of classical hidden Markov model (HMM) for non-convex signals. The search algorithm initially developed for convex duration distributions actually identifies non-convex duration distributions. The gaussian mixture density (GMD) algorithm is used to simulate the non-convex duration distribution, with the search algorithm using sub-state combination. Music signal recognition tests show a 10% percent precision improvement with (1.1%) recall ratio improvement. Therefore, this is an effective model and search algorithm for non-convex duration distribution HMM which can be extended to other non-convex signals.