CNN Model Enhancement for Audio Data
Budget: $30 – $250 USD
I have an existing Convolutional Neural Network (CNN) model built in Python that processes audio data preprocessed using Mel-frequency cepstral coefficients (MFCCs). The model currently delivers high accuracy results, but there's an issue - It consistently provides the same answer when applied to different inputs. I need an expert to investigate and resolve this issue, ensuring that the model provides accurate and varied predictions for different audio inputs.
Key Requirements:
- Review and diagnose the current CNN model.
- Identify why the model outputs the same result for different inputs.
- Implement necessary modifications to rectify the issue.
- Ensure the model maintains its high accuracy while providing diverse predictions for different audio inputs.
Ideal Skills and Experience:
- Proficiency in Python for CNN model development.
- Experience in working with audio data, particularly MFCCs.
- Strong understanding of neural networks, CNNs in particular.
- Ability to debug and fine-tune deep learning models.
- Excellent problem-solving skills and attention to detail.
Key Requirements:
- Review and diagnose the current CNN model.
- Identify why the model outputs the same result for different inputs.
- Implement necessary modifications to rectify the issue.
- Ensure the model maintains its high accuracy while providing diverse predictions for different audio inputs.
Ideal Skills and Experience:
- Proficiency in Python for CNN model development.
- Experience in working with audio data, particularly MFCCs.
- Strong understanding of neural networks, CNNs in particular.
- Ability to debug and fine-tune deep learning models.
- Excellent problem-solving skills and attention to detail.