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Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Segment-Based Signal Typing and Predictive Modeling in Pediatric Dysphonia With Different Vibratory Sources.

Yeonggwang Park¹, Supraja Anand², Susan Baker Brehm^3,4

¹School of Communication Sciences and Disorders, University of Central Florida, Orlando.

Journal of Speech, Language, and Hearing Research : JSLHR

|October 29, 2025

Summary

This summary is machine-generated.

This study refined voice signal typing for children with dysphonia, improving acoustic measure reliability. An automated tool achieved high accuracy in identifying different voice signal types, enhancing clinical use.

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Area of Science:

Speech-language pathology
Acoustic analysis
Voice disorders

Background:

Severe dysphonia in children presents challenges for acoustic analysis due to signal aperiodicity.
Current voice signal typing methods are subjective and may not capture multiple signal types within a sample.

Purpose of the Study:

To refine a manual signal typing tool for segment-level labeling of pediatric voice signals.
To develop an objective, predictive model for automated voice signal typing in children.

Main Methods:

Expert speech-language pathologists manually labeled voice samples from children with glottal and supraglottal vibratory sources.
A predictive model was trained using acoustic measures like pitch strength, EnvSD8, sharpness, and CPPS.
Model performance was assessed using a test set and cross-validation.

Main Results:

Manual typing identified multiple signal types in 11% of samples overall and 20% of samples with supraglottal vibratory sources (SGVS).
The predictive model achieved 81%-96% accuracy in classifying signal types.
Key acoustic measures (EnvSD8, CPPS, sharpness) were effective predictors.

Conclusions:

The refined manual tool enhances signal typing precision, potentially improving acoustic measure reliability and enabling new outcome metrics.
Automated signal typing using objective measures offers significant clinical utility for pediatric voice analysis.