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Related Experiment Video

Updated: May 14, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

A Conformer-Based Time-Frequency Decoupling Network for Pig Vocalization Behavior Classification.

Jianping Wang¹, Yuqing Liu¹, Siao Geng²

¹School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China.

Animals : an Open Access Journal From MDPI

|May 13, 2026

Summary

A new attention-guided acoustic framework (ATF-Conformer) accurately classifies pig vocalizations, enabling continuous, non-invasive health and welfare monitoring in commercial farms.

Keywords:

ATF-Conformer acoustic monitoring behavioral vocalizations pig behavior precision livestock farming vocalization classification

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Area of Science:

Animal Behavior
Machine Learning
Acoustic Sensing

Background:

Continuous pig behavior monitoring is vital for welfare and health in commercial farms.
Vision-based systems face limitations due to barn conditions like poor lighting and occlusion.
Acoustic sensing is a promising alternative but struggles with background noise and sound variability.

Purpose of the Study:

To develop an advanced acoustic framework for accurate pig vocalization classification in commercial farm settings.
To overcome challenges of background noise and temporal variability in pig sounds for behavior monitoring.

Main Methods:

Developed an attention-guided acoustic framework (ATF-Conformer) for pig vocalization classification.
Utilized spectrogram denoising and interactive attention to enhance acoustic signals.
Employed a time-frequency-decoupled Conformer encoder and mask-based temporal pooling for robust feature representation and classification.

Main Results:

ATF-Conformer achieved 97.34% accuracy in a five-class vocalization dataset (cough, scream, estrus, feeding, normal).
The model demonstrated stable performance on an independent test set with 97.38% accuracy.
Outperformed existing acoustic models across multiple evaluation metrics in five-fold cross-validation.

Conclusions:

The ATF-Conformer method enables continuous, non-invasive monitoring of pig behavior through vocalizations.
This technology can assist farm managers in early detection of health issues like frequent coughing.
Supports precision livestock farming by facilitating targeted on-site inspections and improving animal welfare.