keras - Very high classification threshold on CNN output to get good performance. Normal?

Question

I am trying to train a CNN on football games audio to predict highlights. The data is composed of MFCC Spectrograms (https://librosa.org/doc/main/generated/librosa.feature.mfcc.html) of duration t=1s, and rate 10Hz. These MFCC spectrograms (~3000 per game, t=300s of labelled footage) are all labelled: 1 if corresponds to a highlight situation, 0 if corresponds to a lowlight. They are all 32x40 matrices: 40 high for the number of MFC coefficients (see librosa doc) and 32 wide for 32 samples per second.

I am training a CNN on this data. Here's its architecture: CNN architecture

I have a balanced set taken from PSGvMU game, composed of 50% highlight/50% lowlight MFCC spectrograms. This set is split into a 80% balanced training dataset and 20% balanced validation dataset.

I am training my model with 10 epochs, 32 batch size, adam optimizer with lr=0.001. Here are the trainign epochs: Training epochs accuracies and validation accuracies

Every time I test my model on new MFCC spectrograms, the predictions (between 0 and 1) have a very high mean (~0.99+) and the optimal classification threshold (calculated by doing argmax(accuracy | threshold)) is often also very high, typically around 0.99-0.999.

Accuracy as function of classification threshold graph

The problem is that I need to know the true labels to get a good classification threshold and hence good results.

What do you think about my approach? Is there something wrong with my model? Or am I just lacking data/overfitting a lot ?

Answer 1

等待大神答复