Hello everyone! Congratulations to all the participants—each of you is a winner! 💪 This challenge was truly incredible. A huge shoutout 🙌🙌 to Zindi, Umbaji, and TechCabal for making it happen. I had an amazing time taking on this challenge.

A special congratulations to Professor and Nymfree Well done! 🎉

Solution Process

1. Audio Processing: Applied various audio enhancement techniques, such as silence removal, noise reduction, and bandpass filtering, to improve audio quality and provide cleaner input data for the model.
2. Model Architecture: Redesigned the MatchboxNet architecture by integrating residual connections, which enhanced the model's ability to capture complex patterns and improved overall performance and stability.
3. Feature Extraction: Utilized Mel-frequency cepstral coefficients (MFCC) for feature extraction, enabling the model to focus on the most relevant audio features for accurate classification.
4. Modeling: Trained the enhanced MatchboxNet using convolutional layers with residual connections, which improved learning efficiency, boosted classification accuracy, and increased model robustness.

You can find my solution here. If you find it helpful, don't forget to star the repo! ⭐

Thank you once again, and happy learning