Identifying and categorizing urban sounds using deep learning.

Skillset Used : Audio Processing, Librosa, MFCC Feature Extraction, Neural Networks, Deep Learning, Classification, TensorFlow/Keras

🔍 What I did

• Processed 8,000+ urban sound samples to classify them into 10 distinct categories (e.g., sirens, drills, dog barks, etc.).
• Utilized Librosa for MFCC feature extraction, capturing essential sound patterns for classification.
• Designed a neural network with:
  • Three dense layers for feature learning
  • ReLU activations for non-linearity
  • Dropout (0.5) to prevent overfitting and enhance generalization.
• Trained the model to recognize unique audio features, improving classification accuracy.

📈 Impact & Insights

• Enhanced audio recognition capabilities, valuable for noise monitoring, smart city applications, and surveillance.
• Demonstrated the power of deep learning in analyzing and categorizing real-world urban sounds.
• Optimized model architecture, balancing performance and computational efficiency.
• Opened possibilities for further applications like speech recognition and environmental sound analysis.

🚀 Learning Outcomes

• Strengthened expertise in audio processing and feature extraction.
• Gained practical experience in designing and optimizing neural networks for classification tasks.
• Improved understanding of real-world sound data and its applications in AI.
• Explored the intersection of deep learning and urban sound analysis.