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

Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

5.0K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
5.0K
Entropy02:39

Entropy

36.8K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
36.8K
Entropy01:18

Entropy

3.7K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.7K
Harmonic Mean01:09

Harmonic Mean

3.9K
The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
3.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

297
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
297
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.3K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
3.3K

You might also read

Related Articles

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

Sort by
Same author

Time-space dynamics of income segregation in the city of Milan.

PNAS nexus·2025
Same author

Fully Programmable Spatial Photonic Ising Machine by Focal Plane Division.

Physical review letters·2025
Same author

Cities beyond proximity.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2024
Same author

Evolving collaboration, dependencies, and use in the Rust Open Source Software ecosystem.

Scientific data·2022
Same author

Taylor's Law in Innovation Processes.

Entropy (Basel, Switzerland)·2020
Same author

Zipf's, Heaps' and Taylor's Laws are Determined by the Expansion into the Adjacent Possible.

Entropy (Basel, Switzerland)·2020
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Feb 24, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K

Maximum entropy models capture melodic styles.

Jason Sakellariou1,2, Francesca Tria3,4,5,6, Vittorio Loreto1,7,8,9

  • 1SONY CSL, Paris, France.

Scientific Reports
|August 25, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Maximum Entropy model for music generation. It creates new melodies in a specific style by using pairwise interactions, outperforming traditional Markov models and enabling musical innovation.

More Related Videos

Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs
08:44

Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs

Published on: May 9, 2017

16.6K
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

922

Related Experiment Videos

Last Updated: Feb 24, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K
Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs
08:44

Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs

Published on: May 9, 2017

16.6K
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

922

Area of Science:

  • Computational Musicology
  • Machine Learning
  • Digital Signal Processing

Background:

  • Automatic music generation traditionally relies on Markov models, which can suffer from overfitting and require high-order interactions to capture long-range musical structures.
  • Capturing the statistical nuances of musical melodies while avoiding plagiarism is a significant challenge in computational musicology.

Purpose of the Study:

  • To develop a Maximum Entropy model for music generation that emulates the style of a given musical corpus.
  • To demonstrate that complex musical phrases can emerge from simpler, pairwise interactions, reducing model complexity and overfitting.
  • To validate the model's ability to generate novel yet stylistically consistent musical sequences.

Main Methods:

  • Implemented a Maximum Entropy model utilizing a k-nearest neighbor approach with only pairwise interactions.
  • Compared the proposed model against fixed-order and variable-order Markov models for melody generation.
  • Employed a data-compression technique to quantitatively assess the originality and stylistic adherence of generated melodies.

Main Results:

  • The Maximum Entropy model successfully captured the statistics of melodies, generating new sequences in the style of the input corpus.
  • Long-range musical phrases emerged organically from competing pairwise interactions, negating the need for explicit high-order dependencies.
  • The proposed model demonstrated superior performance over traditional Markov models in terms of stylistic emulation and innovation.

Conclusions:

  • Maximum Entropy models based on pairwise interactions offer an effective alternative to high-order Markov models for music generation.
  • This approach allows for the creation of musically sensible variations of existing phrases, fostering innovation in artificial music.
  • The model provides a robust framework for generating novel musical content that balances stylistic fidelity with originality.