Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K
Aggregates Classification01:29

Aggregates Classification

400
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...
400
Classification of Signals01:30

Classification of Signals

975
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...
975

You might also read

Related Articles

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

Sort by
Same author

Exploring AI as a Diagnostic Tool in Medical Imaging for Dermatopathological Diseases.

Indian journal of dermatology·2026
Same author

Novel deep learning solutions with layered recurrent neural networks for nonlinear stiff Dahl hysteresis model in piezoelectric actuator.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Predictive analysis of plankton population dynamics in marine biosphere: a nonlinear ARX neural network for the carbon-thermal-nutrient-plankton asymmetric multifactor system for global warming.

Environmental monitoring and assessment·2025
Same author

Artificial intelligence for fall detection in older adults: A comprehensive survey of machine learning, deep learning approaches, and future directions.

Ageing research reviews·2025
Same author

A Robust and Efficient Workflow for Heart Valve Disease Detection from PCG Signals: Integrating WCNN, MFCC Optimization, and Signal Quality Evaluation.

Sensors (Basel, Switzerland)·2025
Same author

Neuro-computational surrogates for aqueous fractional-order nekton-plankton spatiotemporal dynamics under toxicant stress, refuge efficacy, and nutrient flux modulation.

Water research·2025

Related Experiment Video

Updated: Sep 27, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.1K

A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification.

Vinayak Ravi Joshi1, Kathiravan Srinivasan2, P M Durai Raj Vincent1

  • 1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.

Frontiers in Public Health
|April 11, 2022
PubMed
Summary

This study decodes infant cries using audio analysis. A novel ensemble model accurately predicts cry reasons, offering parents valuable insights into their baby's needs.

Keywords:
MFCCbaby cryfeature vectorsspectrogramsstack-based algorithms

More Related Videos

Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

16.6K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.7K

Related Experiment Videos

Last Updated: Sep 27, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.1K
Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

16.6K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.7K

Area of Science:

  • Infant cry analysis
  • Machine learning for healthcare
  • Signal processing

Background:

  • Interpreting infant cries is challenging for parents.
  • Cries signal various needs like hunger, pain, or discomfort.
  • Audio patterns in cries hold key classification information.

Purpose of the Study:

  • To develop an efficient method for predicting infant cry reasons.
  • To analyze audio features for accurate cry classification.
  • To compare deep learning models with an ensemble approach.

Main Methods:

  • Audio signals converted to spectrograms using Mel-frequency cepstral coefficients (MFCCs).
  • Convolutional Neural Network (CNN) models (VGG16, YOLOv4) used for classification.
  • A multistage heterogeneous stacking ensemble model developed for enhanced classification.

Main Results:

  • The ensemble model outperformed standard CNNs in performance and efficiency.
  • Achieved a high mean classification accuracy of 93.7%.
  • Demonstrated superior overall performance in infant cry analysis.

Conclusions:

  • The proposed ensemble model is highly effective for infant cry reason prediction.
  • This technology can assist parents in understanding infant needs.
  • Advanced ensemble methods offer significant advantages in audio classification tasks.