In STFT, time resolution and frequency resolution is same for particular analysis as windowing with is constant for a particular analysis, but in case of wavelet transform, scaling function changes the frequency resolution and window width correspondingly.
Continuous Wavelet Transform is give as:
$$ S(a, b)= \int f(t) \psi{(\frac{t-a}b}) dt $$
$$ \text{ changing value of b in } \psi{(\frac{t-a}b}) \text{ gives the different frequency band which means signal analysis in instant} $$ $$ \text{ of a with }\frac{1}{ b} \text{ frequency band, that is multiresolutional analysis. } $$
Similarly for digital signal, multi-resolutional analysis is performed by passing signals through analysis filters, where filters composed of high pass filters, band pass filters and low pass filters.
Out of them, output of high pass filters are to be considered as details of the signals and out put of low pass filter is to be considered as approximation. Above structural diagram briefly describe the MRA of discrete signals. d0, d1, d2.... represent details and A0 represents approximation.
Continuous Wavelet Transform is give as:
$$ S(a, b)= \int f(t) \psi{(\frac{t-a}b}) dt $$
$$ \text{ changing value of b in } \psi{(\frac{t-a}b}) \text{ gives the different frequency band which means signal analysis in instant} $$ $$ \text{ of a with }\frac{1}{ b} \text{ frequency band, that is multiresolutional analysis. } $$
Similarly for digital signal, multi-resolutional analysis is performed by passing signals through analysis filters, where filters composed of high pass filters, band pass filters and low pass filters.
Out of them, output of high pass filters are to be considered as details of the signals and out put of low pass filter is to be considered as approximation. Above structural diagram briefly describe the MRA of discrete signals. d0, d1, d2.... represent details and A0 represents approximation.
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