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

Classification of Signals01:30

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-I01:26

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-II01:31

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,
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Force Classification01:22

Force Classification

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,...
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

You might also read

Related Articles

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

Sort by
Same author

Soil bacterial community composition is more stable in kiwifruit orchards relative to phyllosphere communities over time.

Environmental microbiome·2023
Same author

Population genomics of a predatory mammal reveals patterns of decline and impacts of exposure to toxic toads.

Molecular ecology·2022
Same author

pgHMA: Application of the heteroduplex mobility assay analysis in phylogenetics and population genetics.

Molecular ecology resources·2021
Same author

An assembly-free method of phylogeny reconstruction using short-read sequences from pooled samples without barcodes.

PLoS computational biology·2021
Same author

Loss of vocal culture and fitness costs in a critically endangered songbird.

Proceedings. Biological sciences·2021
Same author

Complete mitochondrial genome of the green-lipped mussel, <i>Perna canaliculus</i> (Mollusca: Mytiloidea), from long nanopore sequencing reads.

Mitochondrial DNA. Part B, Resources·2021
Same journal

Interaction of near-wall bubble arrays with acoustic waves induced by an oscillating rigid wall.

The Journal of the Acoustical Society of America·2026
Same journal

Ultra-broadband underwater acoustic projector based on transverse resonance orthogonal beam (TROB) mode and acoustic matching layer technique.

The Journal of the Acoustical Society of America·2026
Same journal

Fine-scale quantitative analysis of bowhead whale (Balaena mysticetus) song shows varying stability of song types.

The Journal of the Acoustical Society of America·2026
Same journal

High-resolution depth estimation for multiple wideband sources in deep sea via sparse Bayesian learninga).

The Journal of the Acoustical Society of America·2026
Same journal

Depression markers in speech: An approach based on tract variables dynamics.

The Journal of the Acoustical Society of America·2026
Same journal

The oyster toadfish (Opsanus tau) alters active and diurnal calling amid vessel noise in New York City.

The Journal of the Acoustical Society of America·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2026

Enhancing an Avian Sound Recognition Model&#39;s Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)
10:55

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Published on: April 11, 2026

Unsupervised bird song syllable classification using evolving neural networks.

Louis Ranjard1, Howard A Ross

  • 1Bioinformatics Institute, School of Biological Sciences, University of Auckland, Auckland 1142, New Zealand. l.ranjard@auckland.ac.nz

The Journal of the Acoustical Society of America
|June 10, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic method for analyzing bird songs, enabling unsupervised classification of syllables. This approach aids in understanding evolutionary relationships and individual identification in passerine bird vocalizations.

More Related Videos

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
10:13

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

Published on: November 26, 2012

Related Experiment Videos

Last Updated: Jul 4, 2026

Enhancing an Avian Sound Recognition Model&#39;s Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)
10:55

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Published on: April 11, 2026

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
10:13

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

Published on: November 26, 2012

Area of Science:

  • Bioacoustics
  • Evolutionary Biology
  • Ornithology

Background:

  • Bird vocalizations evolve under selection pressures and neutral processes due to learning.
  • Passerine bird songs encode information about individual, population, and species relationships.
  • Analyzing these complex vocalizations requires processing large datasets and consistent encoding methods.

Purpose of the Study:

  • To present a novel automatic method for computing syllable distance.
  • To enable unsupervised classification of bird song syllables.
  • To compare the results of this automatic method with human-based analysis for White-crowned Sparrow songs.

Main Methods:

  • Automatic encoding of bird songs into sequences of syllables.
  • Development of a specific algorithm to compute a syllable distance measure.
  • Unsupervised classification of song syllables based on computed distances.
  • Comparison of automated results with human-based analysis.

Main Results:

  • The automatic method successfully computes syllable distances for song analysis.
  • Unsupervised classification of syllables was achieved using the developed algorithm.
  • Results from the automated encoding of White-crowned Sparrow songs were comparable to human analysis.

Conclusions:

  • The presented automatic method offers a reproducible approach to analyzing bird song structure.
  • This method facilitates the study of evolutionary and social signaling in bird vocalizations.
  • Automated analysis provides a valuable tool for bioacoustic research and understanding bird communication.