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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...

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Related Experiment Video

Updated: Jul 16, 2026

Enhancing an Avian Sound Recognition Model&#39;s Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)
10:55

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Published on: April 11, 2026

Overcoming Data Scarcity: Few-Shot Pig Vocalization Recognition via Domain Expansion, Knowledge Transfer, and Feature

Guangbo Li1,2,3, Wenxiu Liu1

  • 1College of Electronic and Information Engineering, Huaibei Institute of Technology, Huaibei 235000, China.

Animals : an Open Access Journal From MDPI
|July 15, 2026
PubMed
Summary

This study introduces PSA-AP, a novel pipeline for pig vocalization recognition. It effectively improves accuracy in few-shot learning scenarios by integrating domain expansion, knowledge transfer, and feature alignment.

Keywords:
ArcFaceSpecAugmentbioacousticsfew-shot learningpig vocalizationself-supervised audio representationspectrogram classification

Related Experiment Videos

Last Updated: Jul 16, 2026

Enhancing an Avian Sound Recognition Model&#39;s Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)
10:55

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Published on: April 11, 2026

Area of Science:

  • Animal Science
  • Machine Learning
  • Bioacoustics

Background:

  • Limited labeled pig vocalization data hinders precision livestock farming.
  • Deep learning models struggle with overfitting in few-shot conditions.
  • Traditional acoustic features may not capture complex sound patterns.

Purpose of the Study:

  • To develop an effective pig-sound adaptation pipeline (PSA-AP) for few-shot learning.
  • To reduce reliance on large labeled datasets for pig vocalization recognition.
  • To enhance the performance of pig sound classification models.

Main Methods:

  • Utilized log-Mel spectrograms for pig sound representation.
  • Integrated SpecAugment for domain expansion.
  • Employed ImageNet-pretrained ResNet18 for knowledge transfer.
  • Applied ArcFace for feature alignment.

Main Results:

  • PSA-AP achieved superior performance across all few-shot settings (K=5 to 30).
  • At K=30, PSA-AP reached 90.60% Accuracy, 90.49% Macro-F1, and 90.60% UAR.
  • The method significantly outperformed the baseline (Raw) by over 7.8 percentage points.

Conclusions:

  • The proposed PSA-AP offers a feasible supervised adaptation strategy for few-shot pig vocalization recognition.
  • The integration of domain expansion, knowledge transfer, and feature alignment is effective.
  • This approach supports non-invasive monitoring in precision livestock farming.