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Author: Markus Erne Co-author: Bernd Edler
Background information: ![]() Figure 1: Aliasing in the time domain and in the frequency domain If the base band spectra and the shifted spectra overlap
(as a result of the multiplication of the time domain signal with an impulse train),
two sine waves with two different frequencies
might result in the same sampled signal after the reconstruction.
![]() Figure 2: Principle of downsampling If the sampling rate has to be reduced, care has to be taken that no aliasing will appear at the new, lower sampling frequency. Therefore a digital pre-filter has to be used in order to band limit the input signal to one half of the lower target sampling rate before any samples may be removed in the decimator. Upsampling: Figure 3: Principle of upsampling If an increased sampling rate is required, new sample points between existing ones have to be created using interpolation techniques. New zero-valued sampling points are filled in between existing sampling points. Values for these intermediate sampling points are then generated by the digital interpolation filter. It is important to understand that upsampling followed by downsampling is not necessarily the same as downsampling followed by upsampling, even if the overall ratio of sampling rates may be the same. Subband coding: Combining upsampling and downsampling, a subband coding system can be derived, showing an analysis part, splitting the input spectrum into the subbands and the synthesis part which fits together all subbands and generates a broadband signal again. ![]() Figure 4: Subband Coding System For the purpose of audio coding, an ideal subband coding system should offer ideal decorrelation of the signals, mimic the critical band behavior of the human auditory system and have no additional overhead. Unfortunately, brickwall bandpass filters in a subband coder can be considered modulated versions [2] of a brickwall prototype lowpass filter and hence are impossible to implement. Nevertheless, critically sampled system (systems in which each subband is sampled at a sampling frequency equal to twice the subband bandwidth) can be built in which aliasing is completely removed even though the individual subband signals may contain aliasing components. Some of these filters offer perfect reconstruction (PR) [1] and others have a non-flat overall frequency response, i.e. they introduce linear distortions. Examples are:
![]() Figure 5: Principle of the TDAC filterbank The time domain signal is windowed using a window length of twice the number of subbands, N, and with a 50% overlap between successive blocks. The windowed signal is transformed using a Discrete Sine Transform (DST) and a Discrete Cosine Transform (DCT) where, after the corresponding inverse transforms, the reconstructed time domain signal will contain aliasing distortion. The aliased terms which, for easier explanation, are shown (dashed line) separately from the signal, are time-reversed. Using a synthesis window and an overlap-add approach, these aliased terms will cancel and perfect reconstruction can be achieved. The following subband filter schemes are used in some current audio coding schemes:
Although aliasing is cancelled in the complete analysis-synthesis system, special problems occur in cases where the subbands are further decomposed in a cascaded filterbank. In these cases, aliasing components generated by downsampling are in the "wrong" passband of the following stage and therefore are not attenuated any further. Examples for subband filter schemes using cascades of filters are: -discrete wavelet transform [4] -wavelet-packets -"hybrid" filterbank of MPEG 1/2 Layer3, which therefore uses an "aliasing reduction" stage The following figures indicate this problem for a wavelet-packet transform.
Figure 6: wavelet packet showing cascaded filters If we look at the ideal and the real subband filter characteristics, it can be noticed that for the resulting subband filter F, sidelobes will appear.
Figure 7: Comparison between an ideal and a real wavelet packet tree iterated filterbank There are two effects of this aliasing: 1.) there are more spectral components to be coded (e.g. a sine wave produces multiple spectral components) and therefore the coding efficiency decreases 2.) quantization noise introduced into a specific subband creates noise at different frequency locations. This is due to the fact that the temporal support of these wavelet coefficients is only of length 10..50 and therefore basis functions of length 50 are used in order to approximate the audio signal. Because FIR filters of length 50 exhibit a limited stopband attenuation, aliasing may appear in the sidelobes of the filter in the stopband regions. These sidelobes may be sufficiently separated from the passband in order to create aliasing in frequency regions with very little signal energy and where the aliasing may not be masked [5].
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