Ryan – Week 4 Status Report

For this week, I began classifying the heart sound training data using a CNN. I researched that CNN’s aren’t very optimal to use for audio files so instead I am taking the spectograms of the data and then training the network on the images of spectograms rather than originally planned as the audio files. I also changed the architecture of the network to now include five convolutional layers that are each followed by a normalization and ReLU layer. The new architecture for my CNN in Matlab code is:

1. convolution2dLayer
2. batchNormalizationLayer
3. reluLayer
4. maxPooling2dLayer
5. convolution2dLayer
6. batchNormalizationLayer
7. reluLayer
8. maxPooling2dLayer
9. convolution2dLayer
10. batchNormalizationLayer
11. reluLayer
12. maxPooling2dLayer
13. convolution2dLayer
14. batchNormalizationLayer
15. reluLayer
16. convolution2dLayer
17. batchNormalizationLayer
18. reluLayer
19. maxPooling2dLayer
20. dropoutLayer
21. fullyConnectedLayer
22. softmaxLayer
23. weightedClassificationLayer

Here is also a visualization of example spectograms of both normal and abnormal heart sounds from the training data:

I then ran the CNN and it achieved an accuracy of 56.52% in 55 seconds to run. I ran it with 25 epochs.

Here is the graph of the training session:

Improvements have to be made to the preprocessing of the data as well as finding a more optimal architecture for this data in the next week.

I am currently on schedule for the week.