Filter bank
From Wikipedia, the free encyclopedia
In signal processing, a filter bank is an array of band-pass filters that separates the input signal into multiple components, each one carrying a single frequency subband of the original signal. One application of a filter bank is a graphic equalizer, which can attenuate the components differently and recombine them into a modified version of the original signal. The process of decomposition performed by the filter bank is called analysis, and the output of analysis is referred to as a subband signal with as many subbands as there are filters in the filter bank. The reconstruction process is called synthesis.
In digital signal processing, the term filter bank is also commonly applied to a bank of receivers. The difference is that receivers also down-convert the subbands to a low center frequency that can be re-sampled at a reduced rate. The same result can sometimes be achieved by undersampling the bandpass subbands.
Another application of filter banks is signal compression, when some frequencies are more important than others. After decomposition, the important frequencies can be coded with a fine resolution. Small differences at these frequencies are significant and a coding scheme that preserves these differences must be used. On the other hand, less important frequencies do not have to be exact. A coarser coding scheme can be used, even though some of the finer details will be lost in the coding.
The vocoder uses a filter bank to determine the amplitude information of the subbands of a modulator signal (such as a voice) and uses them to control the amplitude of the subbands of a carrier signal (such as the output of a guitar or synthesizer), thus imposing the dynamic characteristics of the modulator on the carrier.
[edit] FFT filter banks
A filter bank can be created by performing a sequence of FFTs on overlapping blocks of the input data. A weighting function is applied to each block to control the shape of the frequency responses of the filters. Instead of a conventional FFT window function, the weighting function is the impulse response of an FIR lowpass filter. The wider the shape of the frequency response:
- the more often the FFTs have to be done to satisfy the Nyquist sampling criteria (which is what distinguishes a filter bank from a spectrum analyzer), and
- the fewer filters that are needed to span the input bandwidth.
Eliminating unnecessary filters (i.e. decimation in frequency) can be accomplished most efficiently in the time-domain by summing subblocks of the weighted data-block, resulting in a smaller FFT size.
A special case occurs when, by design, the length of the subblocks is an integer multiple of the interval between FFTs. Then the FFT filter bank can be described in terms of one or more polyphase filter structures where the phases are recombined by an FFT instead of a simple summation. The number of subblocks is the impulse response length (or depth) of each filter.
[edit] See also
[edit] References
- J. Harris, Fredric (2004). Multirate signal processing for communication systems. Upper Saddle River, NJ: Prentice Hall PTR. ISBN 0-13-146511-2.
 |
This signal processing-related article is a stub. You can help Wikipedia by expanding it. |
