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.