Taking the Moving Average of STFT Results I realize that this seems redundant, but I'm getting consistently better results when performing machine learning techniques that use spectral features extracted using the moving average process below, rather than using STFT results alone.  I'm a machine learning guy by trade, and I don't have a background in signal processing.  I'm hoping you guys might be able to guide me in the right direction here.

STFT process:
I use a 10 second STFT window with 9 second overlap. The result is 1 second spaced observations that contain 10 seconds of information.

STFT Moving Average process:
I use a 2.5 second STFT window with a 1/480 second increment (480Hz sample rate), then take a 10 second moving average with a 1 second increment.  The result is 1 second spaced observations that contain 12.5 seconds of information.

My reasoning for using the moving average process was that I get a smoother signal to work with, but I'm not sure that it makes sense to signal processing folks. Is there anything fundamentally wrong with doing things this way besides having to deal with the additional computational cost?

edit: It seems like the reason for the improved results is related to the theory behind using Welch's method for power estimation. Maybe?
As rb-j says in his comment, this is often how the (magnitude-squared) STFT is smoothed $-$ using a moving average window.

The only concern might be that you're using too much smoothing. A 10 second moving average window (resulting in a 12.5 second "effective" window) may mean that some artifacts take a while to show or do not show up at all.