I'm trying to make a few classifiers with scikitlearn. I'm using the Urban Sound database, and trying to replicated their research for a baseline. However, I don't understand how they derive their feature vector? (I've tried to contact them to no avail).
Here is the passage:
In all experiments we extract the features on a per-frame basis using a window size of 23.2 ms and 50% frame overlap. We compute 40 Mel bands between 0 and 22050 Hz and keep the first 25 MFCC coefficients (we do not apply any pre-emphasis nor liftering). The per-frame values for each coefficient are summarized across time using the following summary statistics: minimum, maximum, median, mean, variance, skewness, kurtosis and the mean and variance of the first and second derivatives, resulting in a feature vector of dimension 275 per slice.
So, 11 metrics * 25 MFCC coefficients == 275 features. I have several concerns:
This passage seems to use the word "coefficient" to refer to a vector of coefficients, which I thought was the cepstrum (itself being composed of coefficients which are scalar).
mean, skewness etc are taken on a vector of coefficients (the time series) and return a scalar. But the delta and delta delta return a vector
How do you get the first 25 coefficients for a frame? Compute N and throw away N - 25? How many should N be?
If you have 11 * 25 features/frame (since "The per-frame values for each coefficient are summarized across time ...") then wouldn't you end up with many many more features/slice? Depending on the value of frames/slice? But the article says "resulting in a feature vector of dimension 275 per slice."
The dimensionality is really confusing me here..