Recent separation schemes applied non-negative matrix factorization (NMF), where the magnitude of the Fourier transformed frames is factorized as the product of two non-negative matrices.
Figure 6, extracted from [22], illustrates the cropping process applied to the transformed frame.
Figure 5 presents a transformed frame, where it is possible to observe the loss of information at the borders.
The two transforms were used in a unique way that scatters the watermark bits throughout the transformed frame in order to achieve high degrees of imperceptibility and robustness.
A matrix is then formed by arranging the wavelet coefficients of all detail sub-bands in a unique distributed pattern which scatters the watermark bits throughout the transformed frame to provide a high degree of robustness.
From the time-domain transformed frame, the delay τ l,j (k) is computed by finding the argument κ that maximizes the cross correlation function (s_{e_{l},e_{j}} kappa,k)): tau_{l,j}(k) = underset{-tau_{max} < kappa < tau_{max}}{text{argmax}} left{ s_{e_{l},e_{j}} kappa,k) right}.
We can transform frame +0 into six sets of 2D curves by { x i, y i, z i, x′, y′, z′} vs. i, respectively.
In the linear feature adaptation approach, a sparse linear transform, called cross transform, is used to transform multiple frames of speech coefficients to a new feature space.
Inside a straw-roofed hut, two women sit at wooden looms working the foot treadles, transforming the frames into thrusting locomotives.
These results can be extended without much difficulty into a more complicated set of equations for transforming between frames of reference that are in motion relative to each other with uniform velocities.
