Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Signals

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-II

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,

Classification of Systems-I

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:

Aggregates Classification

Aggregates Classification

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...

Perceiving Loudness, Pitch, and Location

Perceiving Loudness, Pitch, and Location

The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...

You might also read

Related Articles

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

Sort by

Same author

Data-driven sampling strategies for fine-tuning bird detection modelsa).

The Journal of the Acoustical Society of America·2026

Same author

A dataset of insect sounds from 459 species for bioacoustic machine learning.

Scientific data·2026

Same author

Quantum-assisted trustworthiness for the Quantum Internet.

PloS one·2025

Same author

Acoustic Comfort Prediction: Integrating Sound Event Detection and Noise Levels from a Wireless Acoustic Sensor Network.

Sensors (Basel, Switzerland)·2024

Same author

Efficient Speech Detection in Environmental Audio Using Acoustic Recognition and Knowledge Distillation.

Sensors (Basel, Switzerland)·2024

Same author

Sons al Balcó: A Comparative Analysis of WASN-Based <i>L</i> Measured Values with Perceptual Questionnaires in Barcelona during the COVID-19 Lockdown.

Sensors (Basel, Switzerland)·2024

Search research articles

Related Experiment Video

Updated: Oct 12, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Multilabel Acoustic Event Classification Using Real-World Urban Data and Physical Redundancy of Sensors.

Ester Vidaña-Vila¹, Joan Navarro², Dan Stowell³

¹GTM-Grup de Recerca en Tecnologies Mèdia, La Salle Ramon Llull Univeristy, 08022 Barcelona, Spain.

Sensors (Basel, Switzerland)

|November 27, 2021

Summary

This study introduces a two-stage classifier for real-time urban sound monitoring. It reliably identifies multiple simultaneous acoustic events, aiding public administrations in managing noise pollution.

Keywords:

acoustic event classification deep neural networks distributed computing multilabel classification physical redundancy urban sound monitoring

More Related Videos

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Related Experiment Videos

Last Updated: Oct 12, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Environmental Science
Acoustics
Computer Science

Background:

Urban noise pollution poses significant health risks to city dwellers.
Effective urban sound monitoring is crucial for public health and urban planning.
Current methods struggle with reliable, cost-effective identification of multiple simultaneous acoustic events.

Purpose of the Study:

To propose a novel two-stage classifier for real-time identification of up to 21 simultaneous urban acoustic events.
To leverage physical redundancy in wireless acoustic sensor networks for enhanced classification.
To provide a reliable and cost-effective solution for urban noise monitoring.

Main Methods:

A two-stage classification system was developed.
The first stage utilizes a multilabel deep neural network for 4-second audio windows.
The second stage aggregates results from neighboring wireless acoustic sensor nodes.

Main Results:

The system achieves real-time classification results in under 1 second.
Experiments demonstrated good performance in classifying common urban acoustic events.
The system was tested on real-world data using various computing devices.

Conclusions:

The proposed two-stage classifier offers a promising approach for urban sound monitoring.
This technology can empower civic organizations with actionable noise pollution data.
The system contributes to addressing the challenge of identifying multiple simultaneous acoustic sources in urban environments.