Dynamical Theory Formalism for Robust Modeling of Damped, Undamped, and Nonlinear Oscillatory Signals
2007
The paper explores a novel framework for signal representation based on dynamic information in a signal that is well suited for robust analysis of low SNR signals and extraction of time-varying features. The method is derived from dynamical theory but formulated in a basic parameter estimation paradigm. Modeling the changes in data provides a compact depiction of time-variant and invariant information plus features related to data dynamics. The method also provides strong noise mitigation properties even when noise statistics is poorly understood. The signal processing formulation supplies a connection between the time-delay and the Fourier domains. This connection helps us bridge non-linear dynamical and signal processing theories and brings a powerful novel tool to signal analysis at large. The experiment is presented using a speech sample from the TIMIT database.
Keywords:
- Speech processing
- Fourier analysis
- Signal processing
- Estimation theory
- Mathematical optimization
- Volterra series
- Signal-to-noise ratio
- Computer science
- Machine learning
- Feature extraction
- Control theory
- Fourier transform
- Artificial intelligence
- Pattern recognition
- Robustness (computer science)
- Algorithm
- Speech recognition
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
4
References
1
Citations
NaN
KQI