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--- |
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language: en |
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tags: |
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- audio-classification |
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- causal-representation |
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- infant-cry-detection |
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license: mit |
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datasets: |
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- custom-audio-dataset |
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metrics: |
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- event-based-f1 |
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- iou |
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- accuracy |
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--- |
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# Infant Cry Detection Using Causal Temporal Representation |
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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. |
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## Features |
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- **Causal Data Generating Process**: Incorporates mathematical causal assumptions to define the relationship between audio features and annotations. |
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- **Supervised Models**: Includes pre-trained state-of-the-art models: |
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- Bidirectional LSTM |
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- Transformer |
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- MobileNet V2 |
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- **Event-Based Metrics**: Tailored for time-sensitive detection tasks: |
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- Event-based F1-score |
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- Intersection over Union (IOU) |
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- **Interactive Example**: Jupyter Notebook with step-by-step usage demonstrations. |
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--- |
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## How to Use |
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You can load the model directly from Hugging Face: |
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```python |
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