Table of Contents
Probabilistic Characterization and Synthesis of Complex Driven Systems
PPT Slide
outline
why bother building a digital violin?
…violin model as a benchmark test for algorithms
context and history of an idea: embedding synthesis
embedding synthesis (2) : why we need a more sophisticated representation?
available machine learning tools
… need for a better learning tool
Part I Cluster-weighted modeling
non-linear function approximation
cluster-weighted modeling (CWM)
model architecture (1)
model architecture (2): forecast of y and ?y
model estimation: expectation-maximization (EM) (Dempster et al. ’77)
EM for Gaussian mixtures (1)
EM for Gaussian mixtures (2)
evaluation and system characterization
evaluation and characterization (2)
a cluster-weighted input-output hidden Markov model (1)
a cluster-weighted input-output hidden Markov model (2)
PPT Slide
Part II Synthesis architectures and applications
classification theory
cluster-weighted classification
application I: stimulus detection from MEG data
application II: consumer fraud characterization
application III: electronic noses
prediction and estimation theory
application I: prediction of the M3-competition
application I: prediction of the M3-competition data (2)
application II:microwave device characterization
microwave device characterization (2)
Part III Data-driven modeling of musical instruments
why would we want digital instruments?
musical synthesis
the digital stradivarius
modeling sequence
sensor hardware - schematic
PPT Slide
interfaces
input patterns
analysis patterns
representations (1): sinusoidal analysis-synthesis
representations (2) - wavetable synthesis ? cluster-weighted sampling
cluster-weighted sampling (2)
experimental results : sinusoidal synthesis
experimental results : wavetable and mixed synthesis
contributions (1)
contributions (2)
conclusions and future work
acknowledgements (1)
acknowledgements (2)
Ende
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