- Enhancing the Signal-to-Noise Ratio (SNR): This can be done by either increasing the power of the signal or reducing the noise level. Techniques like filtering or amplification might be helpful.
- Using Noise Reduction Techniques: Applying algorithms that are specifically designed to reduce or filter out noise can improve the clarity of the signal in the spectrogram.
- Improving the Model's Robustness to Noise: If you're using a machine learning model, training it with a wider range of noise levels might help it become more robust to such conditions.
- Advanced Signal Processing Techniques: Techniques such as wavelet denoising, spectral subtraction, or adaptive filtering could be considered, depending on the specific requirements and nature of your data.
- Re-evaluating the Model's Parameters or Architecture: If the model is not performing well, consider revisiting its architecture or parameters. Maybe a different model type or a tweak in the existing model's configuration might yield better results.
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