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

1.2K
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...
1.2K
Force Classification01:22

Force Classification

2.0K
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,...
2.0K
Neural Circuits01:25

Neural Circuits

2.2K
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...
2.2K
Classification of Systems-I01:26

Classification of Systems-I

449
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:
449
Classification of Systems-II01:31

Classification of Systems-II

380
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,
380
Aggregates Classification01:29

Aggregates Classification

564
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...
564

You might also read

Related Articles

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

Sort by
Same author

Psychological and Physiological Responses of Residents to Floor Impact Noise in Multifamily Housing.

Noise & health·2026
Same author

The efficacy of immunotherapy in glioma requires distal B cell responses in tumor-draining lymph nodes.

Science immunology·2026
Same author

Green tea-derived exosome-like nanoparticles attenuate oxidative stress-induced skin senescence via modulation of p38 MAPK signaling.

Frontiers in pharmacology·2026
Same author

Evaluating the impact of a rapid response system on survival of patients with cancer undergoing emergency surgery for acute abdomen: A single-center retrospective cohort study.

PloS one·2026
Same author

Araliadiol Protects Human Keratinocytes From Oxidative Stress, DNA Damage, and Apoptosis via Activation of Antioxidant Signaling.

Frontiers in bioscience (Landmark edition)·2026
Same author

Effects of noise on health-related quality of life: The roles of outdoor noise, indoor noise, and noise sensitivity.

Journal of exposure science & environmental epidemiology·2025

Related Experiment Video

Updated: Nov 24, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

204

Inter-floor noise classification using convolutional neural network.

Hye-Kyung Shin1, Sang Hee Park1, Kyoung-Woo Kim1

  • 1Department of Living and Built Environment Research, Korea Institute of Civil Engineering and Building Technology, Goyang-Si, Kyeonggi-Do, Korea.

Plos One
|December 22, 2020
PubMed
Summary

This study uses a convolutional neural network to automatically classify inter-floor noise sources, improving accuracy and efficiency over manual methods. ResNet achieved the highest performance, offering a promising solution for managing noise disputes in apartment buildings.

More Related Videos

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.5K

Related Experiment Videos

Last Updated: Nov 24, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

204
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.5K

Area of Science:

  • Acoustics
  • Machine Learning
  • Signal Processing

Background:

  • Inter-floor noise in apartments causes disputes and requires manual, time-consuming verification against legal standards.
  • Current manual noise source identification lacks consistency and efficiency.

Purpose of the Study:

  • To develop an automated method for classifying inter-floor noise by source using a convolutional neural network (CNN).
  • To evaluate the performance of various CNN models in distinguishing indoor noise from external sources.

Main Methods:

  • A dataset of 1,515 annotated sound sources from three households was created, focusing on six common inter-floor noise types.
  • CNN models (DenseNet, ResNet, Inception, EfficientNet) were trained and tested on the inter-floor noise dataset and the ESC50 urban sound dataset.
  • Performance was evaluated using accuracy, F1 score, precision, recall, and inference time.

Main Results:

  • CNN models achieved high accuracy (91.43-95.27%) in classifying inter-floor noise.
  • ResNet demonstrated superior performance with 95.27±2.30% accuracy and the best F1 score, precision, recall, and shortest inference time.
  • The models successfully distinguished indoor noise from external urban sounds.

Conclusions:

  • CNNs, particularly ResNet, offer an effective and efficient solution for automated inter-floor noise classification.
  • This technology can enhance the monitoring of indoor soundscapes and aid in resolving noise-related disputes.