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  - No Noise Addition: Training without additional noise leads to better feature retention.
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  - Library: Keras is used for training and architecture implementation.
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  - Sample Rate: A consistent sample rate of 16,000 Hz was maintained for all preprocessing steps.
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- ![Confusion Matrix](./pics/confusion-matrix.png)
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  2. Confusion Matrix Analysis
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  - High Precision: Minimal false positives suggest the model is very specific when identifying emergencies.
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  - High Recall: Minimal false negatives indicate that most emergencies are correctly identified.
 
 
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  ## Model Usage
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  - `Silver Assistant` Project
 
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  - No Noise Addition: Training without additional noise leads to better feature retention.
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  - Library: Keras is used for training and architecture implementation.
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  - Sample Rate: A consistent sample rate of 16,000 Hz was maintained for all preprocessing steps.
 
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  2. Confusion Matrix Analysis
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  - High Precision: Minimal false positives suggest the model is very specific when identifying emergencies.
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  - High Recall: Minimal false negatives indicate that most emergencies are correctly identified.
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+ ![Confusion Matrix](./pics/confusion-matrix.png)
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+
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  ## Model Usage
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  - `Silver Assistant` Project