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By using a frequency footprint to differentiate each sensor, awaking all the sensors except for the one of interest is avoided.
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For example, different frequency footprints can be used to reconstruct signals (male/female voices), or different appearance in the time domain, like notches, give some hope of distinguishing two mixed signals.
Figure 4 Time-frequency footprint selection.
(16) Figure 2 T-shaped time-frequency footprint.
Finally, it is worth mentioning that a square-shaped time-frequency footprint have been also considered.
Repeat steps 1 to 5 for each set of features, time-frequency footprint, and SNR. .
The relative behavior of the different sizes of the time-frequency footprint is the same for the different SNRs.
The six different sets of features are evaluated using a time-frequency footprint with the same number of neighbor frequencies and time frames, Nfreqs = Nframes.
Average WDO value obtained by the non-quantified classifier using different combinations of features and different sizes of the time-frequency footprint, with Nfreqs = Nframes.
Create the matrix Q calculating the features corresponding to the evaluated set and time-frequency footprint, using the data from the design set.
Create the matrix Q with the features of SET2 and the time-frequency footprint evaluated, using the data from the design set. 2.
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Justyna Jupowicz-Kozak
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