Before an analog voice signal can be stored digitally, it must be converted to a
digital signal. This is done in multiple steps.
Figure 1. Example Analog Signal
First, the analog signal (an example shown in
Figure 1-1), is converted to a time-discrete signal by taking
periodic samples, as shown in
Figure 1-2. The time interval between two samples is called the “sampling period”
and its reciprocal is called the "sampling frequency”. According to the Nyquist-Shannon
sampling theorem, the sampling frequency has to be at least double the maximum frequency
component present in the sampled signal. Otherwise, the periodic continuation of the
signal in the frequency domain would result in spectral overlap, called “aliasing”. An
aliased signal can not be uniquely reconstructed from its samples.
Figure 2. Discrete Time Signal
A speech signal contains its major information below 3 kHz; therefore a low-pass filter
can be used to band-limit the signal. For an ideal low-pass filter with a cut-off
frequency of 3 kHz, the sampling frequency must be 6 kHz or more. Depending on the
filter, the filter slope is more or less steep. For a first-order filter like the RC
filter used in this application, it is particularly necessary to choose a much higher
sampling frequency. The upper limit is set by the features of the Analog-to-Digital
Converter (ADC). See ADC Data Acquisition Configuration for details on how this has
been implemented in the application.
Determining the digital values that represent the analog samples taken at this sampling
frequency is called quantization. The analog signal is quantized by assigning an analog
value to the nearest allowed digital value, as shown in
Figure 1-3. The number of available digital values is called
“resolution” and is always limited to the resolution of the ADC being used (for example,
an 8-bit ADC can have up to 256 values). Therefore, the quantization of the analog
signals always results in a loss of information. This quantization error is inversely
proportional to the resolution of the digital signal. It is also inversely proportional
to the dynamic range of the signal, or the range between minimum and maximum values. The
conversion range of the ADC can be adjusted to the dynamic range of the signal by
setting the voltage reference to a maximum value suitable for the application.
Figure 3. Quantized Signal
Alternatively, a microphone amplifier can be designed to cover the ADC dynamic range.
Both methods reduce the quantization error.
Figure 1-4 shows the digital values
that represent the analog signal. These are the values that are read as ADC conversion
results and can be stored in memory.
Figure 4. Digital Signal
