Why normalization kills my accuracy
There could a skewed power envelope or non-stationary data. As a result, off-the-shelf feature scaling could attenuate the signal.

There are feature scaling techniques that tend to work better for audio signals, examples include: RMS level (Root Mean Square Level), Cepstral Mean Subtraction (CMS), RelAtive SpecTrAl (RASTA), kernel filtering, short time gaussianization, stochastic matching, and feature warping. 

You should make sure you understand your raw data and the assumptions of each feature scaling technique before application. Accuracy-driven machine learning might lead to the wrong conclusions.