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...

You might also read

Related Articles

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

Sort by

Same author

Multi-Dimensional Resources Management with GNN for Adaptive Routing Optimization.

Sensors (Basel, Switzerland)·2026

Same author

DuSAFNet: A Multi-Path Feature Fusion and Spectral-Temporal Attention-Based Model for Bird Audio Classification.

Animals : an open access journal from MDPI·2025

Same author

CAR T- cell therapy provides an opportunity for further consolidation treatment for relapsed or refractory adult Burkitt lymphoma patients.

Frontiers in oncology·2025

Same author

Enhancing hydrolysis acidification process for coal gasification wastewater treatment through micro-aeration strategy: The role of micro-oxygen environment.

Water research·2025

Same author

Aerobic exercise alleviates chronic allergic airway inflammation by regulating the circMETTL9/EIF4A3/IGFBP3 axis.

Cellular signalling·2025

Same author

Needs and Experiences of Older Adults With Multiple Morbidities Using Health Technologies for Self-Management: A Systematic Review and Meta-Synthesis.

Worldviews on evidence-based nursing·2025

Search research articles

Related Experiment Video

Updated: Aug 19, 2025

Pupillometry to Assess Auditory Sensation in Guinea Pigs

Pupillometry to Assess Auditory Sensation in Guinea Pigs

Published on: January 6, 2023

Combined spectral and speech features for pig speech recognition.

Xuan Wu¹, Silong Zhou¹, Mingwei Chen¹

¹College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

|December 1, 2022

Summary

This study introduces a novel pig sound classification method using both spectrogram and time-domain audio features. The dual-feature approach significantly improves accuracy in identifying pig states for better health monitoring.

More Related Videos

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

Related Experiment Videos

Last Updated: Aug 19, 2025

Pupillometry to Assess Auditory Sensation in Guinea Pigs

Pupillometry to Assess Auditory Sensation in Guinea Pigs

Published on: January 6, 2023

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

Area of Science:

* Acoustics
* Computer Vision
* Animal Science

Background:

* Pig vocalizations are crucial indicators of health, hunger, and emotional states.
* Traditional speech recognition methods using only spectral features may limit accuracy in pig sound classification.
* A need exists for advanced methods to accurately interpret pig sounds for timely health interventions.

Purpose of the Study:

* To develop and validate a more accurate pig sound classification method.
* To enhance the monitoring of pig health and welfare through acoustic analysis.
* To establish a comprehensive dataset for future research in pig vocalization analysis.

Main Methods:

* A novel pig sound classification method leveraging both signal spectrum (spectrograms) and time-domain audio features.
* Implementation of a parallel network structure to process and integrate dual feature inputs.
* Selection and combination of the best-performing network model and classifier.

Main Results:

* Achieved a classification accuracy of 93.39% on the pig sound classification task.
* Reached an Area Under the Curve (AUC) of 0.99163, demonstrating high model performance.
* Established a dataset of 4,000 pig sound samples across four categories.

Conclusions:

* The proposed dual-feature method significantly outperforms traditional single-feature approaches for pig sound classification.
* This research provides a robust foundation for automated pig health monitoring systems.
* The established dataset will facilitate further advancements in animal acoustics and welfare research.