Although there is a significant body of work on the classification of time-series [59,60], existing methods such as HMM [57] and dynamic time-warping (DTW) [61] mainly address variations in the time axis between samples, and rely on a fixed front-end processing (segmentation and feature extraction), so do not adequately address the variations in temporal and spectral resolution. The fields of time-frequency distributions and wavelet analysis address these issues for analysis, but are not statistical models. Although the segmental HMM [62] is a step in the right direction, it still is based on fixed front-end processing. Now, with the introduction of the class-specific feature theorem [63], [10], [9], and later the probability density function (PDF) projection theorem (PPT) [6,5], the freedom now exists to use different feature sets together in a common statistical model. The class-specific HMM allows each state in a HMM uses a different feature [3]. The multi-resolution HMM (MR-HMM) goes even further, allowing different analysis window lengths to be used simultaneously [64,65,66,20] .

Baggenstoss 2017-05-19