CAPYLEE/CRSTC

Infant Cry Detection Using Causal Temporal Representation

This model detects infant cries using a novel causal temporal representation framework. By integrating causal reasoning into the data-generating process (DGP), the model aims to enhance the interpretability and reliability of cry detection systems.

Features

• Causal Data Generating Process: Incorporates mathematical causal assumptions to define the relationship between audio features and annotations.
• Supervised Models: Includes pre-trained state-of-the-art models:
  • Bidirectional LSTM
  • Transformer
  • MobileNet V2
• Event-Based Metrics: Tailored for time-sensitive detection tasks:
  • Event-based F1-score
  • Intersection over Union (IOU)
• Interactive Example: Jupyter Notebook with step-by-step usage demonstrations.

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How to Use

You can load the model directly from Hugging Face: