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

Neural Regulation01:37

Neural Regulation

34.8K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
34.8K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

967
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
967

You might also read

Related Articles

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

Sort by
Same author

Recent Advances in Carbon-Based Materials for Adsorptive and Photocatalytic Antibiotic Removal.

Nanomaterials (Basel, Switzerland)·2022
Same author

Pyrolysis of <i>Aesculus chinensis</i> Bunge Leaves as for Extracted Bio-Oil Material.

Polymers·2022
Same author

An automated DNA computing platform for rapid etiological diagnostics.

Science advances·2022
Same author

Development, characterization and probiotic encapsulating ability of novel Momordica charantia bioactive polysaccharides/whey protein isolate composite gels.

International journal of biological macromolecules·2022
Same author

Exogenous 24-epibrassinolide boosts plant growth under alkaline stress from physiological and transcriptomic perspectives: The case of broomcorn millet (Panicum miliaceum L.).

Ecotoxicology and environmental safety·2022
Same author

Neutrophilic granulocyte percentage is associated with anxiety in Chinese hospitalized heart failure patients.

BMC cardiovascular disorders·2022

Related Experiment Video

Updated: May 1, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

11.0K

Music recognition based on diffractive neural networks.

Xingcen Ge1, Zirui Feng2, Qian Ma2

  • 1Communication University of China, Nanjing, China.

Iscience
|April 30, 2026
PubMed
Summary

Diffractive neural networks (DNNs) can now process time-series audio for music genre and sentiment classification. This hardware-efficient AI approach shows high accuracy, promising advancements in intelligent signal processing.

Keywords:
artificial intelligencecomputer scienceoptical signal processing

Related Experiment Videos

Last Updated: May 1, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

11.0K

Area of Science:

  • Artificial Intelligence
  • Signal Processing
  • Optical Computing

Background:

  • Diffractive neural networks (DNNs) offer efficient computation for AI hardware.
  • Their use in time-series signal processing is an underexplored area.
  • Existing methods may lack speed and energy efficiency for complex data.

Purpose of the Study:

  • To propose an electromagnetic diffractive neural network framework for music style and sentiment classification.
  • To evaluate the DNNs' performance on time-series audio data.
  • To demonstrate DNNs as a viable solution for intelligent signal processing.

Main Methods:

  • Audio signals were converted into log-Mel spectrograms.
  • A multilayer metasurface-based DNN processed the spectrograms.
  • Experiments were conducted on the GTZAN and POP909 datasets.

Main Results:

  • Achieved 90.3% accuracy for five-genre music classification (GTZAN).
  • Achieved 96.33% accuracy for three-genre music classification (GTZAN).
  • Attained 90.56% accuracy for sentiment classification (POP909).

Conclusions:

  • Diffractive neural networks effectively process time-series data.
  • DNNs offer a promising hardware-efficient solution for intelligent signal processing.
  • This framework has potential applications in communication and sensing.