Convolution now gets easier with these two methods for a practical input
signal, which has a quite big length. The OA and OS methods are implemented
using FFT. The larger length input signal gets decomposed into desired length
smaller signals.
For OAM, we use linear convolution using circular convolution. The OSM first overlaps the smaller input signals and then, after obtaining individual smaller output signals, y1, y2,.., etc., the overlapped part is neglected and the remaining elements of the output signals are then clubbed to obtain the actual output signal. Thus, a conclusion can be made that OAM and OSM are block processing techniques and are suitable for real-time implementation of signal processing.
For OAM, we use linear convolution using circular convolution. The OSM first overlaps the smaller input signals and then, after obtaining individual smaller output signals, y1, y2,.., etc., the overlapped part is neglected and the remaining elements of the output signals are then clubbed to obtain the actual output signal. Thus, a conclusion can be made that OAM and OSM are block processing techniques and are suitable for real-time implementation of signal processing.
What is the difference between OAM and OSM techniques?
ReplyDeleteIn OAM , each signal data block contains only as many samples as allows circular convolution to be equivalent to linear convolution. The signal data block is zero-padded prior to the FFT to prevent the filter impulse response from “wrapping around” the end of the sequence.
DeleteIn OSM , no zero-padding is performed on the input data, thus the circular convolution is not equivalent to linear convolution. The portions that “wrap around” are useless and discarded. To compensate for this, the last part of the previous input block is used as the beginning of the next block. OSM requires no addition of transients, making it faster than OAM.
For long signals(1024 samples) direct FFT implementation will take more time. Hence, OSM and OAM methods find applications in practical implementation.
ReplyDeleteconceptually good
ReplyDeleteinformative
ReplyDeleteConcise explanation
ReplyDeleteGood content
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