This building-block considers the linear reduction of
``Gaussian-like" data, corresponding to row
``Gaussian" in Table 3.1. By this, we mean sensor data
such as raw or averaged acoustic or seismic data
that is not constrained to be positive.
Approximate Gaussianity is usually a result of naturally-occuring
averaging (central-limit theorem) effects.
We may also consider spectral or
intensity data that is averaged or non-linearly transformed
(i.e. the logarithm), or both. An example is the last stage
in the calculation of MFCC coefficients (truncated DCT of
the log band energies). The ES is formed from the sum
of the squares of the input samples.
Applications are very wide including principal component analysis
and linear filtering.
Linear transformation of real data