Acoustic Scene Classification
Modernized legacy architectures to PyTorch, leveraging torchaudio for GPU-accelerated Mel-spectrogram feature extraction to train classification models across multiple environmental sound classes. Established a robust evaluation framework utilizing 4-fold cross-validation and automated experiment tracking with Weights & Biases (W&B) to monitor loss curves and model convergence. Conducted systematic ablation studies to optimize inference speed and classification precision, analyzing the impact of sampling rates and He-Kaiming initialization.
Technologies Used
Problem Statement
The proliferation of audio-enabled IoT devices and smart city infrastructure has created a massive influx of environmental audio data. However, extracting actionable intelligence from this unstructured data remains computationally expensive and highly inaccurate with legacy systems. Businesses struggle to implement reliable audio surveillance, predictive maintenance, or context-aware smart environments because existing models fail to generalize across noisy, real-world acoustic scenes.
Solution
This project revitalizes environmental audio classification by modernizing legacy architectures into a highly optimized, GPU-accelerated PyTorch framework. By utilizing torchaudio for Mel-spectrogram feature extraction and implementing rigorous 4-fold cross-validation, the solution provides a scalable and robust mechanism for acoustic scene recognition. This enables businesses to deploy efficient, context-aware audio intelligence systems—such as security monitoring or smart home automation—with higher accuracy and lower inference latency.
Key Features
Modernized PyTorch architecture for acoustic scene classification
GPU-accelerated Mel-spectrogram feature extraction via torchaudio
Robust evaluation framework with 4-fold cross-validation
Automated experiment tracking and visualization using Weights & Biases (W&B)
Systematic ablation studies analyzing sampling rates and He-Kaiming initialization
Engineering Challenges
Translating legacy architectures and reproducing baseline metrics in modern PyTorch
Handling variable-length audio signals efficiently during GPU batching
Optimizing hyperparameter combinations to prevent overfitting on complex acoustic data
Results & Metrics
Significantly improved model convergence speed through He-Kaiming initialization
Optimized inference speed and classification precision compared to legacy baselines
Established a highly reproducible, tracked experimental pipeline
Lessons Learned
Proper weight initialization is critical for deep networks trained on noisy audio data
GPU-accelerated feature extraction (torchaudio) removes significant CPU bottlenecks
Automated tracking (W&B) is indispensable for organizing complex ablation studies