1. Sampling rate conversion by a rational factor

2. Changing the Sampling rate using discrete-time processing
downsampling; sampling rate compressor;

3. Frequency domain of downsampling
Since this is a ‘re-sampling’ process. Remember that, from continuous-time sampling of x[n]=xc(nT), we have
Similarly, for the down-sampled signal xd[m]=xc(mT’), (where T’ = MT), we have

4. Frequency domain of downsampling
We are interested in the relation between X(ejw) and Xd(ejw). Let’s represent r as r = i + kM, where 0  i  M1, (i.e., r  i (mod M)). Then

5. Frequency domain of downsampling
Therefore, the downsampling can be treated as a ‘re-sampling’ process. It s frequency domain relationship is similar to that of the D/C converter as:
This is equivalent to compositing M copies of the of X(ejw), frequency scaled by M and shifted by inter multiples of 2.
The aliasing can be avoided by ensuring that X(ejw) is bandlimited as

6. Example of downsampling in the Frequency domain (without aliasing)
Sampling with a sufficiently large rate which avoids aliasing

7. Example of downsampling in the Frequency domain (without aliasing)
Downsampling by 2 (M=2)

8. Downsampling with prefiltering to avoid aliasing (decimation)
From the above, the DTFT of the down-sampled signal is the superposition of M shifted/scaled versions of the DTFT of the original signal.
To avoid aliasing, we need wN</M, where wN is the highest frequency of the discrete-time signal x[n].
Hence, downsampling is usually accompanied with a pre-low-pass filtering, and a low-pass filter followed by down-sampling is usually called a decimator, and termed the process as decimation.

9. Up-sampling : Upsampling; sampling rate expander. or equivalently,
In frequency domain: :

10. Example of up-sampling
Upsampling in the frequency domain

11. Up-sampling with post low-pass filtering
Similar to the case of D/C converter, upsampoling is usually companied with a post low-pass filter with cutoff frequency /L and gain L, to reconstruct the sequence.
A low-pass filter followed by up-sampling is called an interpolator, and the whole process is called interpolation.

12. Example of up-sampling followed by low-pass filtering
Applying low-pass filtering

13. Interpolation Similar to the ideal D/C converter,
If we choose an ideal lowpass filter with cutoff frequency /L and gain L, its impulse response is
Hence
Its an interpolation of the discrete sequence x[k]

14. Sampling rate conversion by a non-integer rational factor
By combining the decimation and interpolation, we can change the sampling rate of a sequence.
Changing the sampling rate by a non-integer factor T’ = TM/L.
Eg., L=100 and M=101, then T’ = 1.01T.

15. Changing the Sampling rate using discrete-time processing
Since the interpolation and decimation filters are in cascade, they can be combined as shown above.

16. Digital Processing of Analog Signals
Pre-filtering to avoid aliasing
It is generally desirable to minimize the sampling rate.
Eg., in processing speech signals, where often only the low-frequency band up to about 3-4k Hz is required, even though the speech signal may have significant frequency content in the 4k to 20k Hz range.
Also, even if the signal is naturally bandlimited, wideband additive noise may fll in the higher frequency range, and as a result of sampling. These noise components would be aliased into the low frequency band.

17. Over-sampled A/D conversion
The anti-aliasing filter is an analog filter. However, in applications involving powerful, but inexpensive, digital processors, these continuous-time filters may account for a major part of the cost of a system.
Instead, we first apply a very simple anti-aliasing filter that has a gradual cutoff (instead of a sharp cutoff) with significant attenuation at MN. Next, implement the C/D conversion at the sampling rate higher than 2MN. After that, sampling rate reduction by a factor of M that includes sharp anti-aliasing filtering is implemented in the discrete-time domain.

18. Using over-sampled A/D conversion to simplify a continuous-time anti-aliasing filter

19. Example of over-sampled A/D conversion

20. Example of over-sampled A/D conversion

21. Sample and hold

22. Example of sample and hold

23. Quantizer (Quantization)
The real-valued signal has to be stored as a code for digital processing. This step is called quantization.
The quantizer is a nonlinear system.
Typically, we apply two’s complement code for representation.

24. Quantizer (Quantization)

25. Quantizer (Quantization)
In general, if we have a (B+1)-bit binary two’s complement fraction of the form:
then its value is
The step size of the quantizer is
where Xm is the full scale level of the A/D converter.
The numerical relationship beween the code words and the quantizer samples is

26. Example of quantization

27. Analysis of quantization errors
In general, for a (B+1)-bit quantizer with step size , the quantization error satisfies that
when
If x[n] is outside this range, then the quantization error is larger in magnitude than /2, and such samples are saided to be clipped.

28. Analysis of quantization errors
Analyzing the quantization by introducing an error source and linearizing the system:
The model is equivalent to quantizer if we know e[n].

29. Assumptions about e[n]
e[n] is a sample sequence of a stationary random process.
e[n] is uncorrelated with the sequence x[n].
The random variables of the error process e[n] are uncorrelated; i.e., the error is a white-noise process.
The probability distribution of the error process is uniform over the range of quantization error (i.e., without being clipped).
The assumptions would not be justified. However, when the signal is a complicated signal (such as speech or music), the assumptions are more realistic.
Experiments have shown that, as the signal becomes more complicated, the measured correlation between the signal and the quantization error decreases, and the error also becomes uncorrelated.

30. Example of quantization error
original signal
3-bit quantization result
3-bit quantization error

31. Example of quantization error
8-bit quantization error
In a heuristic sense, the assumptions of the statistical model appear to be valid if the signal is sufficiently complex and the quantization steps are sufficiently small, so that the amplitude of the signal is likely to traverse many quantization steps from sample to sample.

32. Quantization error analysis
e[n] is a white noise sequence. The probability density function of e[n] is

33. Quantization error analysis
The mean value of e[n] is zero, and its variance is
Since
For a (B+1)-bit quantizer with full-scale value Xm, the noise variance, or power, is

34. Quantization error analysis
A common measure of the amount of degradation of a signal by additive noise is the signal-to-noise ratio (SNR), defined as the ratio of signal variance (power) to noise variance. Expressed in decibels (dB), the SNR of a (B+1)-bit quantizer is
Hence, the SNR increases approximately 6dB for each bit added to the world length of the quantized samples.

35. Quantization error analysis
The equation can be further simplified for analysis. For example, if the signal amplitude has a Gaussian distribution, only percent of the samples would have an amplitude greater than 4x.
Thus to avoid clipping the peaks of the signal (as is assumed in our statistical model), we might set the gain of filters and amplifiers preceding the A/D converter so that x = Xm/4. Using this value of x gives
For example, obtaining a SNR about dB in high-quality music recording and playback requires 16-bit quantization.
But it should be remembered that such performance is obtained only if the input signal is carefully matched to the full-scale of the A/D converter.