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Related Concept Videos

Sampling Methods: Overview01:06

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Related Experiment Video

Updated: Feb 23, 2026

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
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Mixed modeling for irregularly sampled and correlated functional data: Speech science applications.

Marianne Pouplier1, Jona Cederbaum2, Philip Hoole1

  • 1Institute of Phonetics and Speech Processing, Ludwig Maximilians University, Munich, Germany.

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|September 3, 2017
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Summary

Functional linear mixed models analyze complex curve data in speech sciences, preserving temporal information lost in traditional methods. This approach enables holistic evaluation of signal dynamics for diverse experimental designs.

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Area of Science:

  • Speech Sciences
  • Statistics
  • Data Analysis

Background:

  • Speech science research often uses complex experimental designs with multiple covariates and random effects.
  • Traditional methods for curve-like data (e.g., time-varying signals) rely on single-time-point feature extraction, leading to information loss.
  • Existing statistical models may struggle with the complex correlation structures inherent in repeated measures data.

Purpose of the Study:

  • To introduce and discuss the application of functional linear mixed models (FLMMs) as a powerful tool for analyzing curve-like data.
  • To demonstrate how FLMMs allow for the holistic evaluation of curve dynamics, preserving temporal information.
  • To highlight the model's utility in handling complex correlation structures arising from repeated measures.

Main Methods:

  • Application of functional linear mixed models (FLMMs), a functional extension of linear mixed models.
  • Nonparametric, spline-based estimation technique accommodating irregularly or sparsely observed correlated functional data.
  • Functional principal component analysis (FPCA) for parsimonious data representation and variance decomposition.

Main Results:

  • FLMMs enable the preservation of information within the temporal domain of curve-like data.
  • The spline-based estimation allows for flexibility in handling data observed at irregular or sparse time points.
  • Functional principal component analysis provides an effective method for dimensionality reduction and understanding variance.

Conclusions:

  • Functional linear mixed models offer a robust statistical framework for analyzing grouped curve data in speech sciences and beyond.
  • This method overcomes limitations of traditional feature extraction techniques by analyzing entire curve dynamics.
  • The approach is broadly applicable to various time series data, including articulatory and acoustic signals, modeled using penalized splines.