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

Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

865
Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...
865
Classification of Systems-I01:26

Classification of Systems-I

696
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:
696
Classification of Systems-II01:31

Classification of Systems-II

577
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,
577
Classification of Signals01:30

Classification of Signals

1.6K
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...
1.6K
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

60.4K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
60.4K
Larynx01:21

Larynx

6.5K
The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
Anatomy of the Larynx
The larynx consists of various components, including cartilage, muscles, and vocal cords. Its structure includes three large unpaired cartilages—the thyroid, cricoid, and epiglottis—and three smaller paired cartilages—the arytenoids,...
6.5K

You might also read

Related Articles

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

Sort by
Same author

Diversity and Spatiotemporal Activity Patterns of Medium and Large Mammals in the Niokolo-Koba National Park, Senegal.

Ecology and evolution·2026
Same author

Characterization of post-COVID syndrome by self-perceived symptom severity stratified by infection wave: beyond COVID, a prospective, multicenter cohort study in Germany.

Infection·2026
Same author

Encoding performance of cortical neurons critically depends on their morphological and neurophysiological properties.

PLoS biology·2026
Same author

Pinpointing the politics of passing away. An empirical ethics case study on legislating assisted dying.

BMC medical ethics·2026
Same author

Disparate social structures are underpinned by distinct social rules across a primate radiation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

PriMAT: Robust multi-animal tracking of primates in the wild.

PloS one·2026

Related Experiment Video

Updated: Apr 14, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

1.0K

Characterizing Vocal Repertoires--Hard vs. Soft Classification Approaches.

Philip Wadewitz1, Kurt Hammerschmidt2, Demian Battaglia3

  • 1Cognitive Ethology Laboratory, German Primate Center, Göttingen, Germany; Theoretical Neurophysics, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany; Bernstein Center for Computational Neuroscience, Göttingen, Germany.

Plos One
|April 28, 2015
PubMed
Summary
This summary is machine-generated.

Objective characterization of animal vocal repertoires is key for understanding communication. Fuzzy clustering effectively reveals graded structures within chacma baboon calls, outperforming traditional methods and factor analysis.

More Related Videos

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

2.1K
fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
11:15

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

Published on: May 23, 2017

7.7K

Related Experiment Videos

Last Updated: Apr 14, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

1.0K
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

2.1K
fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
11:15

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

Published on: May 23, 2017

7.7K

Area of Science:

  • Bioacoustics
  • Animal Communication
  • Behavioral Ecology

Background:

  • Objective characterization of animal vocal repertoires is crucial for understanding acoustic communication.
  • Current methodologies lack standardization, impacting classification and analysis of call structures.
  • There's a need for robust methods to analyze the gradation within and between animal call types.

Purpose of the Study:

  • To address challenges in standardizing vocal repertoire analysis.
  • To compare clustering methods for classifying animal calls.
  • To investigate the utility of fuzzy clustering for analyzing graded vocal structures.

Main Methods:

  • Analysis of 912 chacma baboon (Papio ursinus) calls.
  • Extraction of 118 acoustic variables and creation of feature sets (9, 38, 118 variables).
  • Comparison of k-means, hierarchical, and fuzzy clustering algorithms, including principal component analysis-derived factors.

Main Results:

  • Clustering accuracy improved with a higher number of acoustic features.
  • Factor analysis reduced resolution of call types.
  • Fuzzy clustering provided a detailed, quantitative description of call gradation, revealing subtypes within call types.
  • No single hard clustering method strongly supported a specific cluster solution.

Conclusions:

  • Fuzzy clustering is recommended for analyzing the graded structure of vocal repertoires.
  • Factor analysis for reducing acoustic variables in clustering should be discouraged.
  • The study highlights the importance of nuanced methods for capturing the complexity of animal communication.