Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Methods of Classification and Identification

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Explainability and Trust in Deep Learning for Cancer Imaging: Systematic Barriers, Clinical Misalignment, and a Translational Roadmap.

Cancers·2026

Same author

RayFlex: Inducing Weight Perception Through Raycast Pseudo-Haptics in Virtual Reality.

IEEE transactions on visualization and computer graphics·2026

Same author

An integrated AI-enabled system using One Class Twin Cross Learning for early gastric cancer detection.

Frontiers in oncology·2026

Same author

Deep learning in multi-modal breast cancer data fusion: a literature review.

Quantitative imaging in medicine and surgery·2025

Same author

Enhancing bone radiology images classification through appropriate preprocessing: a deep learning and explainable artificial intelligence approach.

Quantitative imaging in medicine and surgery·2025

Same author

A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning.

Sensors (Basel, Switzerland)·2025

Same journal

The effect of glycyrrhetinic acid on pharmacokinetics of cortisone and its metabolite cortisol in rats.

Journal of biomedicine & biotechnology·2012

Same journal

Insights and hopes in umbilical cord blood stem cell transplantations.

Journal of biomedicine & biotechnology·2012

Same journal

Three-dimensional visualization with large data sets: a simulation of spreading cortical depression in human brain.

Journal of biomedicine & biotechnology·2012

Same journal

Bioconversion of sugarcane biomass into ethanol: an overview about composition, pretreatment methods, detoxification of hydrolysates, enzymatic saccharification, and ethanol fermentation.

Journal of biomedicine & biotechnology·2012

Same journal

Trends in tissue engineering for blood vessels.

Journal of biomedicine & biotechnology·2012

Same journal

Salinomycin as a drug for targeting human cancer stem cells.

Journal of biomedicine & biotechnology·2012

See all related articles

Search research articles

Related Experiment Video

Updated: May 22, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Using hierarchical time series clustering algorithm and wavelet classifier for biometric voice classification.

¹Department of Computer and Information Science, University of Macau, Taipa, Macau. ccfong@umac.mo

Journal of Biomedicine & Biotechnology

|May 24, 2012

Summary

This summary is machine-generated.

This study introduces new computational algorithms for voice classification, offering a more transparent alternative to existing methods. These algorithms effectively group unlabelled voice samples for various applications.

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Related Experiment Videos

Last Updated: May 22, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Area of Science:

Speech processing
Machine learning
Biometrics

Background:

Voice biometrics are widely used for verification and identification.
Voice classification, grouping unlabelled voice samples, is less researched but has emerging applications.
Existing voice classification methods often lack transparency.

Purpose of the Study:

To propose novel computational algorithms for voice classification.
To offer a more interpretable alternative to 'black box' methods like ANNs and SVMs.
To demonstrate the effectiveness of the proposed algorithms.

Main Methods:

A combination of hierarchical clustering, dynamic time warp transform, discrete wavelet transform, and decision tree algorithms.
Utilizing both synthetic and empirically collected datasets for validation.
Comparing proposed methods against existing voice identification and verification techniques.

Main Results:

The proposed algorithms provide a transparent and interpretable approach to voice classification.
Effectiveness demonstrated on both synthetic and real-world voice data.
Successfully groups unlabelled voice samples for various classification tasks.

Conclusions:

The developed algorithms offer a viable and interpretable solution for voice classification.
This research contributes to advancing the field of voice analysis beyond traditional identification.
The methods show promise for applications in phone monitoring and speaker attribute classification.