Proposed model

This repository contains a project using the CNN+LLM scheme to improve the classification of heartbeat sounds.
The physionet 2016 dataset was used as the main data for training and validation: https://archive.physionet.org/challenge/2016/.

The idea of this project is inspired by the following paper: https://arxiv.org/abs/2303.17489

Here is schematic diagram of the proposed pipeline:

Achieved results.

0.904 UAR with CNN(PANNs) only, as in the following paper: https://ieeexplore.ieee.org/document/9175450.
0.941 UAR with proposed pipeline.

Usage and reproduction.

Here are the steps you should follow to reproduce the results:

1. Obtain heartbeats data from physionet 2016 challenge (https://archive.physionet.org/challenge/2016/)
2. Do preprocessing of the data (1-channel 16khz audio, train and test directories in ./data/physionet)
3. Use python llm_parallel_train.py -n "your_exp_name" to train the model. Note, that you should choose number of gpus for training in this line: world_size = 4, and select them in this line: os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3".

Check ./models/PrefixLLM.py to take a look at the implementation of the model.
Check ./datahandlers/MakeDataset.py and ./datahandlers/CNNDataLoader.py to adjust data processing if necessary.
Check ./llm_classification/LLMTrainer.py to setup experiment settings.
Check pipeline.ipynb to find evaluation tools and more info about the project.