The engineering problem
Audio models need consistent input preparation as well as a trained network. This artifact provides an example of heart-sound classification for research and education, with preprocessing and integration documented alongside the model.
Scope and contribution
The model is published on the owner-confirmed Hugging Face profile. This case note summarizes the public model card, not a client deployment. The specific personal contribution, training-data provenance, and reproducibility record require further documentation.
Architecture
Audio recording → preprocessing → convolutional network → class scores → application output.
The card describes a PyTorch CNN and FastAPI integration. Input consistency is a central engineering concern: sampling, duration, and normalization affect the model's behavior.
Constraints
Recording devices, background noise, and differences between data collections can change performance. The interface needs to make unsupported formats and poor-quality recordings visible rather than silently returning a confident result.
Evaluation status
The website has not reproduced training or evaluation. No independently verified accuracy, sample count, or clinical validation is presented here. A future technical review should document the dataset split, class balance, leakage controls, and per-class errors.
Limits and next steps
This is research and educational work, not a diagnostic service. Next engineering steps include reproducible preprocessing tests, an explicit model-version record, and evaluation across recording conditions. The portfolio does not accept audio uploads or run this model.
Inspect the artifact
Read the model card and integration notes.
Source reviewed on 9 October 2026. The case illustrates supporting model engineering, separately from the Agentic AI service offer.