MR-HMM illustrative example

For illustration, we created an MR-HMM with three sub-classes: background noise, low-frequency (LF) burst, and high-frequency (HF) burst. Feature extraction consisted of autoregressive (AR) analysis with model order depending on the sub-class (See Section 10.4.10). In Figure 14.3 (top), we see a spectrogram exhibiting an LF and an HF noise burst. In the center of the figure are the a posteriori proxy state probabilities $\gamma_i(m)$. These are summed, collecting all wait states in each sub-class, to obtain the sub-class probabilities (bottom). Note that at some times, there is considerable uncertainty about which trellis path is in effect as evidenced by probability sharing. But, the path probabilities sum up to provide a crisp decision about the sub-class.

Figure 14.3: Illustration of MR-HMM using synthetic data.