2021.09 – 2022.06
Lightweight Environmental Sound Classification & Edge Deployment
STAR project narrative
Situation
Target embedded hardware (M5Stack V2unit) had severely constrained compute and memory, demanding a highly optimized model and deployment workflow for real-time environmental sound classification.
Task
Architect and deliver a complete, reproducible model training-to-edge-deployment pipeline targeting an embedded Linux device.
Action
Engineered a Mel spectrogram feature extraction pipeline on the ESC-50 dataset with audio augmentation; trained a CNN classifier in PyTorch using 5-fold cross-validation and conducted Adam vs. SGD optimizer ablation; converted the trained model via PyTorch → ONNX → NCNN and cross-compiled the inference runtime for the M5Stack V2unit via SSH.
Result
Delivered a fully operational edge inference system with stable real-time performance on embedded hardware, validating the complete PyTorch → ONNX → NCNN cross-compilation workflow.
Core skills and stack
Interview focus
- Full PyTorch → ONNX → NCNN conversion and cross-compilation pipeline
- Adam vs. SGD optimizer ablation study with TensorBoard visualization