In this lab, we will study two related concepts in digital systems: quantization and filtering.
Previously, we have seen that we could not represent a continuous-time signal on a digital computer. Instead, we were forced to sample in time to get a discrete-time signal.
Similarly, we cannot exactly represent the amplitude of a given sample on a
digital computer, since we only have a limited number of bits for that
representation. If we have 
bits, we can only have 
possible values
for the amplitude. But if is large enough, or if we have a computer that
has a floating-point implementation, the approximate value will most likely be
good enough for the application at hand.
However, on a smaller, lower cost platform, such as a fixed-point DSP chip, we may be limited to a much smaller set of possible amplitude values. This process of reducing an infinite set of values to a finite set is called quantization. Quantization effects are quite pronounced when the number of bits available for representation is very low.
In lecture, we discussed the frequency domain representation of a signal. In the second part of this lab, we will filter speech and music samples with various filters to determine their effect upon the signal. We will also look at the frequency content of various signals and try to reconstruct those signals.