The paper aims to identify hidden Markov model parameters. unobservable state represents a finite-state jump process. observations contain Wiener noise with state-dependent intensity. identified parameters include the transition intensity matrix of system state, conditional drift and diffusion coefficients in observations. We propose an iterative identification algorithm based on fixed-interval...

When listeners hear a target signal in the presence of competing sounds, they are quite good at extracting information at instances when the local signal-to-noise ratio of the target is most favorable. Previous research suggests that listeners can easily understand a periodically interrupted target when it is interleaved with noise. It is not clear if this ability extends to the case where an i...