Analog-to-Digital and Digital-to-Analog Voice Encoding

This section covers the fundamentals of digitally encoding voice, specifically, the basics of voice digitization and the various compression schemes that are used to transport voice while using less bandwidth.

Digitizing speech was a project first undertaken by the Bell System in the 1950s. The original purpose of digitizing speech was to deploy more voice circuits with a smaller number of wires. This evolved into the T1 and E1 transmission methods of today. Examples of analog and digital waveforms are presented in Figure 2-24.


Table 2-3 details the steps to convert an analog signal to a digital signal.


The three mandatory components in the analog-to-digital conversion process are further described as follows:

• Sampling Sample the analog signal at periodic intervals. The output of sampling is a pulse amplitude modulation (PAM) signal.

• Quantization Match the PAM signal to a segmented scale. This scale measures the amplitude (height) of the PAM signal and assigns an integer number to define that amplitude.

• Encoding Convert the integer base-10 number to a binary number. The output of encoding is a binary expression in which each bit is either a 1 (pulse) or a 0 (no pulse).

This three-step process is repeated 8000 times per second for telephone voice-channel service. Use the fourth optional step, compression, to save bandwidth. This optional step allows a single channel to carry more voice calls.

After the receiving terminal at the far end receives the digital PCM signal, it must convert the PCM signal back into an analog signal. The process of converting digital signals back into analog signals includes the following two processes:

• Decoding The received 8-bit word is decoded to recover the number that defines the amplitude of that sample. This information is used to rebuild a PAM signal of the original amplitude. This process is simply the reverse of the analog-to-digital conversion.

• Filtering The PAM signal is passed through a filter to reconstruct the original analog wave form from its digitally coded counterpart.

With this basic understanding of analog to digital conversion, this chapter considers the sampling, quantization, and encoding processes more thoroughly, beginning with sampling.


Sampling and the Nyquist Theorem

One of the major issues with sampling is determining how often to take those samples (that is, "snapshots") of the analog wave. You do not want to take too few samples per second because when the equipment at the other end of the phone call attempts to reassemble and make sense of those samples, a different sound (that is, a lower frequency sound) signal might also match those samples, and the incorrect sound would be heard by the listener. This phenomenon is called aliasing, as shown in Figure 2-25.


With the obvious detrimental effect of undersampling, you might be tempted to take many more samples per second. While that approach, sometimes called oversampling, does indeed eliminate the issue of aliasing, it also suffers from a major drawback. If you take far more samples per second than actually needed to accurately recreate the original signal, you consume more bandwidth than is absolutely necessary. Because bandwidth is a scarce commodity (especially on a wide-area network), you do not want to perform the oversampling shown in Figure 2-26.


Digital signal technology is based on the premise stated in the Nyquist Theorem: When a signal is instantaneously sampled at the transmitter in regular intervals and has a rate of at least twice the highest channel frequency, then the samples will contain sufficient information to allow an accurate reconstruction of the signal at the receiver. Figure 2-27 illustrates sampling, as prescribed by the Nyquist Theorem.


While the human ear can sense sounds from 20 to 20,000 Hz, and speech encompasses sounds from about 200 to 9000 Hz, the telephone channel was designed to operate at about 300 to 3400 Hz. This economical range carries enough fidelity to allow callers to identify the party at the far end and sense their mood. Nyquist decided to extend the digitization to 4000 Hz, to capture higher-frequency sounds that the telephone channel may deliver. Therefore, the highest frequency for voice is 4000 Hz, or 8000 samples per second; that is, one sample every 125 microseconds.


Quantization

Quantization involves dividing the range of amplitude values that are present in an analog signal sample into a set of discrete steps that are closest in value to the original analog signal, as illustrated in Figure 2-28. Each step is assigned a unique digital code word.


In Figure 2-28, the x-axis is time and the y-axis is the voltage value (PAM). The voltage range is divided into 16 segments (0 to 7 positive, and 0 to 7 negative). Starting with segment 0, each segment has fewer steps than the previous segment, which reduces the signal-to-noise ratio (SNR) and makes the segment uniform. This segmentation also corresponds closely to the logarithmic behavior of the human ear. If there is an SNR problem, it is resolved by using a logarithmic scale to convert PAM to PCM.

Linear sampling of analog signals causes small-amplitude signals to have a lower SNR, and therefore poorer quality, than larger amplitude signals. The Bell System developed the µ-law method of quantization, which is widely used in North America. The International Telecommunication Union (ITU) modified the original m-law method and created a-law, which is used in countries outside of North America.

By allowing smaller step functions at lower amplitudes, rather than higher amplitudes, µ-law and a-law provide a method of reducing this problem. Both µ-law and a-law "compand" the signal; that is, they both compress the signal for transmission and then expand the signal back to its original form at the other end.