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In the Type 1 case [link] , [link] ,

h ( n ) = 2 M sin π 4 M ( 2 n + 1 ) .

and therefore

h i ( n ) = 1 2 M sin π ( i + 1 ) ( n + . 5 ) M - ( 2 i + 1 ) π 4 - sin π i ( n + . 5 ) M - ( 2 i + 1 ) π 4 .

In the Type 2 case [link] ,

h ( n ) = 2 M sin π 2 M ( n + 1 ) ,

and hence

h i ( n ) = 1 2 M sin π ( i + 1 ) ( n + 1 ) M - ( 2 i + 1 ) π 4 - sin π i ( n + 1 ) M - ( 2 i + 1 ) π 4 .

Linear phase filter banks

In some applications. it is desirable to have filter banks with linear-phase filters [link] . The linear-phase constraint (like the modulation constraint studied earlier) reduces the numberof free parameters in the design of a filter bank. Unitary linear phase filter banks have been studied recently [link] , [link] . In this section we develop algebraic characterizations of certain typesof linear filter banks that can be used as a starting point for designing such filter banks.

In this section, we assume that the desired frequency responses are as in [link] . For simplicity we also assume that the number of channels, M , is an even integer and that the filters are FIR. It should be possible to extend the results that follow to the case when M is an odd integer in a straightforward manner.

Consider an M -channel FIR filter bank with filters whose passbands approximate ideal filters.Several transformations relate the M ideal filter responses. We have already seen one example where all the ideal filters are obtained bymodulation of a prototype filter. We now look at other types of transformations that relate the filters. Specifically,the ideal frequency response of h M - 1 - i can be obtained by shifting the response of the h i by π . This either corresponds to the restriction that

h M - 1 - i ( n ) = ( - 1 ) n h i ( n ) ; H M - 1 - i ( z ) = H i ( - z ) ; H M - 1 - i ( ω ) = H i ( ω + π ) ,

or to the restriction that

h M - 1 - i ( n ) = ( - 1 ) n h i ( N - 1 - n ) ; H M - 1 - i ( z ) = H i R ( - z ) ; H M - 1 - i ( ω ) = H i ( ω + π )

where N is the filter length and for polynomial H ( z ) , H R ( z ) denotes its reflection polynomial (i.e. the polynomial with coefficients in the reversedorder). The former will be called pairwise-shift (or PS) symmetry (it is also known as pairwise-mirror image symmetry [link] ) , while the latter will be called pairwise-conjugated-shift (or PCS) symmetry (also known as pairwise-symmetry [link] ). Both these symmetries relate pairs offilters in the filter bank. Another type of symmetry occurs when the filters themselves are symmetric or linear-phase. The only type of linear-phase symmetry we will consider isof the form

h i ( n ) = ± h i ( N - 1 - n ) ; H i ( z ) = ± H i R ( z ) ,

where the filters are all of fixed length N , and the symmetry is about N - 1 2 . For an M -channel linear-phase filter bank (with M an even integer), M / 2 filters each are even-symmetric and odd-symmetric respectively [link] .

We now look at the structural restrictions on H p ( z ) , the polyphase component matrix of the analysis bank that these three types of symmetries impose.Let J denote the exchange matrix with ones on the antidiagonal. Postmultiplying a matrix A by J is equivalent to reversing the order of the columns of A , and premultiplying is equivalent to reversing the order of the rows of A . Let V denote the sign-alternating matrix, the diagonal matrix of alternating ± 1 's. Postmultiplying by V , alternates the signs of the columns of A , while premultiplying alternates the signs of the rows of A . The polyphase components of H ( z ) are related to the polyphase components of H R ( z ) by reflection and reversal of the ordering of the components. Indeed, if H ( z ) is of length M m , and H ( z ) = l = 0 M - 1 z - l H l ( z M ) , then,

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Source:  OpenStax, Wavelets and wavelet transforms. OpenStax CNX. Aug 06, 2015 Download for free at https://legacy.cnx.org/content/col11454/1.6
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