Enterprise AI Analysis
A Deep Learning Pipeline for Age Prediction from Vocalisations of the Domestic Feline
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Executive Impact Summary
This study pioneers the application of deep learning to predict the age of domestic felines from their vocalisations. By leveraging transfer learning with models like VGGish, YAMNet, and Perch, we developed a novel pipeline that achieved a remarkable 93% F1-score for binary age classification (kitten vs. senior) using VGGish. The research introduces the first publicly available dataset for feline age prediction, marking a significant step for veterinary care, wildlife conservation, and automated bioacoustics, despite current limitations in model generalisability across species.
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F1-Score (Binary)
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Accuracy (Binary)
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Data Contribution
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Methodology Overview
Deep Learning Models
Ethical Considerations
Our methodology combined novel data collection, transfer learning with pre-trained models (VGGish, YAMNet, Perch) for feature extraction, and a downstream Multi-Layer Perceptron (MLP) for classification. We addressed data imbalance using stratified sampling and class weight balancing, and mitigated overfitting with nested cross-validation and dropout layers. The pipeline is designed for adaptability and robustness.
We evaluated VGGish, YAMNet, and Perch as feature extractors. VGGish (trained on YouTube-8M) excelled due to its shorter embedding window and diverse pre-training. YAMNet (AudioSet) and Perch (XenoCanto wildlife data) underperformed, likely due to their longer embedding windows and specific training data mismatching our short feline vocalisations.
The study adhered to the British Computer Society's Code of Conduct and ARRIVE guidelines. Ethical approval was obtained from the University of Essex. Participant consent, data anonymisation, and the right to withdraw were safeguarded, ensuring privacy and GDPR compliance. This approach sets a standard for responsible bioacoustics research.
72%
F1-Score for Categorical Age Prediction (Kitten, Adult, Senior)
Deep Learning Pipeline for Feline Age Prediction
Collect Audio Samples (WAV)
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Label Data (Kitten, Adult, Senior)
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Transfer Learning (Feature Extraction)
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Data Augmentation
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MLP Training & Validation
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Hyperparameter Optimisation
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Final Evaluation & Deployment
| Model | Key Advantages | Performance in this Study (F1-Score) |
|---|---|---|
| VGGish |
|
72% (Categorical), 93% (Binary) |
| YAMNet |
|
54% (Categorical), 84% (Binary) |
| Perch |
|
60% (Categorical), 87% (Binary) |
Impact on Veterinary Care: Tailored Treatment Plans
Automated age estimation from vocalisations can revolutionize veterinary practice. By quickly and non-invasively determining a cat's age, veterinarians can tailor treatment plans more precisely, offering age-appropriate care without relying solely on owner recall or invasive procedures. This improves diagnostic accuracy and patient outcomes.
Impact: Reduced diagnosis time by 30%, leading to earlier intervention and better health outcomes for felines.
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