5 research outputs found
Markov Switching
Markov switching models are a popular family of models that introduces
time-variation in the parameters in the form of their state- or regime-specific
values. Importantly, this time-variation is governed by a discrete-valued
latent stochastic process with limited memory. More specifically, the current
value of the state indicator is determined only by the value of the state
indicator from the previous period, thus the Markov property, and the
transition matrix. The latter characterizes the properties of the Markov
process by determining with what probability each of the states can be visited
next period, given the state in the current period. This setup decides on the
two main advantages of the Markov switching models. Namely, the estimation of
the probability of state occurrences in each of the sample periods by using
filtering and smoothing methods and the estimation of the state-specific
parameters. These two features open the possibility for improved
interpretations of the parameters associated with specific regimes combined
with the corresponding regime probabilities, as well as for improved
forecasting performance based on persistent regimes and parameters
characterizing them.Comment: Keywords: Transition Probabilities, Exogenous Markov Switching,
Infinite Hidden Markov Model, Endogenous Markov Switching, Markov Process,
Finite Mixture Model, Change-point Model, Non-homogeneous Markov Switching,
Time Series Analysis, Business Cycle Analysi
Markov Switching
Markov switching models are a popular family of models that introduces
time-variation in the parameters in the form of their state- or regime-specific
values. Importantly, this time-variation is governed by a discrete-valued
latent stochastic process with limited memory. More specifically, the current
value of the state indicator is determined only by the value of the state
indicator from the previous period, thus the Markov property, and the
transition matrix. The latter characterizes the properties of the Markov
process by determining with what probability each of the states can be visited
next period, given the state in the current period. This setup decides on the
two main advantages of the Markov switching models. Namely, the estimation of
the probability of state occurrences in each of the sample periods by using
filtering and smoothing methods and the estimation of the state-specific
parameters. These two features open the possibility for improved
interpretations of the parameters associated with specific regimes combined
with the corresponding regime probabilities, as well as for improved
forecasting performance based on persistent regimes and parameters
characterizing them.Comment: Keywords: Transition Probabilities, Exogenous Markov Switching,
Infinite Hidden Markov Model, Endogenous Markov Switching, Markov Process,
Finite Mixture Model, Change-point Model, Non-homogeneous Markov Switching,
Time Series Analysis, Business Cycle Analysi
Applying source separation to music
International audienceSeparation of existing audio into remixable elements is very useful to repurpose music audio. Applications include upmixing video soundtracks to surround sound (e.g. home theater 5.1 systems), facilitating music transcriptions, allowing better mashups and remixes for disk jockeys, and rebalancing sound levels on multiple instruments or voices recorded simultaneously to a single track. In this chapter, we provide an overview of the algorithms and approaches designed to address the challenges and opportunities in music. Where applicable, we also introduce commonalities and links to source separation for video soundtracks, since many musical scenarios involve video soundtracks (e.g. YouTube recordings of live concerts, movie sound tracks). While space prohibits describing every method in detail, we include detail on representative musicâspecific algorithms and approaches not covered in other chapters. The intent is to give the reader a highâlevel understanding of the workings of key exemplars of the source separation approaches applied in this domain