I have a binary car sound classifier. I have a feature set that is extracted from audio with size of 48. I have a model(multi layer neural network) that has around %90 accuracy on test and validation sets. (without normalization or Standardization)
I see that the feature values are mostly around [-10, +10] But there are certain features with a mean of 4000. Seeing unproportional values within features I though some feature scaling might improve things. So using scikit-learn tools I tried:
- Simply removing the means from features - Normalizer - Min max scaler - Robust Scaler
And all these above ended up dropping my accuracy to ~ %50! (%100 recall, %50 precision)
So how is this possible? And what is the correct way to normalize my data?