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Sampling Continuous Time Signal01:11

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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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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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A Novel Source-Filter Stochastic Model for Voice Production.

E Cataldo1, L Monteiro1, C Soize2

  • 1Universidade Federal Fluminense, Graduate program in Electrical and Telecommunications Engineering, Niterói, RJ, Brazil.

Journal of Voice : Official Journal of the Voice Foundation
|January 5, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel stochastic voice production model, unifying existing deterministic models. The model generates and controls jitter for normal and pathological voices, validated through inverse problem analysis.

Keywords:
JitterStochastic modelsVoice pathologiesVoice production

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Area of Science:

  • * Acoustics and Voice Production
  • * Biomedical Engineering
  • * Signal Processing

Background:

  • * Existing voice production models often neglect vocal fold and vocal tract coupling.
  • * Deterministic models can be complex and limited in simulating voice pathologies.
  • * Stochastic modeling offers potential for more realistic voice signal generation.

Observation:

  • * A novel stochastic voice production model is proposed, integrating the source-filter Fant theory.
  • * The model unifies two deterministic one mass-spring-damper models.
  • * It generates voice signals without explicit vocal fold-vocal tract coupling.

Findings:

  • * The developed stochastic model successfully generates and controls jitter levels.
  • * It can simulate hoarse voice signals and pathological voice characteristics.
  • * Model parameters were identified and validated for both normal and vocal fold paralysis cases.

Implications:

  • * This simpler stochastic model provides a new tool for voice synthesis and analysis.
  • * It facilitates the study of voice pathologies like hoarseness and paralysis.
  • * Offers a more accessible approach to understanding voice signal variations.