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inherent in human speech to reduce the amount of information required. The technique still relies on PCM encoding, but adds an additional step to carry out its task. The 64-Kbps PCM-encoded signal is fed into an ADPCM transcoder, which considers the prior behavior of the incoming stream to predict the behavior of the next sample. Here s where the magic happens: instead of transmitting the actual value of the predicted sample, it encodes in 4 bits and transmits the difference between the actual and predicted samples. Since the difference from sample to sample is typically quite small, the results are generally considered to be very close to toll-quality. This 4-bit transcoding process, which is based on the known behavior characteristics of human voice, enables the system to transmit 8,000 4-bit samples per second, thus reducing the overall bandwidth requirement from 64 to 32 Kbps. It should be noted that ADPCM works well for voice, because the encoding and predictive algorithms are based upon its behavior characteristics. It does not, however, work as well for higher bit rate data (above 4800 bps), which has an entirely different set of behavior characteristics.
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Continuously Variable Slope Delta (CVSD)
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Continuously Variable Slope Delta (CVSD) is a unique form of voice encoding that relies on the values of individual bits to predict the behavior of the incoming signal. Instead of transmitting the volume (height or y-value) of PAM samples, CVSD transmits information that measures the changing slope of the waveform. So, instead of transmitting the actual change itself, it transmits the rate of change. To perform its task, CVSD uses a reference voltage to which it compares all incoming values. If the incoming signal value is less than the reference voltage, the CVSD encoder reduces the slope of the curve to make its approximation better mirror the slope of the actual signal. If the incoming value is more than the reference value, then the encoder will increase the slope of the output signal, again causing it to approach and therefore mirror the slope of the actual signal. With each recurring sample and comparison, the step function can be increased or decreased as required. For example, if the signal is increasing rapidly, then the steps are increased one after the other in a form of step function by the encoding algorithm. Obviously, the reproduced signal is not a particularly exact representation of the input signal. In practice, it is pretty jagged. Filters therefore are used to smooth the transitions.
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Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website.
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CVSD is typically implemented at 32 Kbps, although it can be implemented at rates as low as 9600 bps. At 16 to 24 Kbps, recognizability is still possible. Down to 9600, recognizability is seriously affected, although intelligibility is not.
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We mention Linear Predictive Coding (LPC) here only because it has carved out a niche for itself in certain voice-related applications such as voice mail systems, automobiles, aviation, and electronic games that speak to children. LPC is a complex process, implemented completely in silicon, which enables voice to be encoded at rates as low as 2400 bps. The resulting quality is far from toll-quality, but it is certainly intelligible and its low bit rate capability gives it a distinct advantage over other systems. LPC relies on the fact that each sound created by the human voice has unique attributes, such as frequency range, resonance, and loudness, among others. When voice samples are created in LPC, these attributes are used to generate prediction coefficients. These predictive coefficients represent linear combinations of previous samples, hence the name, Linear Predictive Coding. Prediction coefficients are created by taking advantage of the known formants of speech, which are the resonant characteristics of the mouth and throat, which give speech its characteristic timbre and sound. This sound, referred to by speech pathologists as the buzz, can be described by both its pitch and its intensity. LPC therefore models the behavior of the vocal cords and the vocal tract itself. To create the digitized voice samples, the buzz is passed through an inverse filter that is selected based upon the value of the coefficients. The remaining signal, after the buzz has been removed, is called the residue. In the most common form of LPC, the residue is encoded as either a voiced or unvoiced sound. Voiced sounds are those that require vocal cord vibration, such as the g in glare, the b in boy, or the d and g in dog. Unvoiced sounds require no vocal cord vibration, such as the h in how, the sh in shoe, or the f in frog. The transmitter creates and sends the prediction coefficients, which include measures of pitch, intensity, and whatever voiced and unvoiced coefficients are required. The receiver undoes the process. It converts the voice residue, pitch, and intensity coefficients into a representation of the source signal, using a filter similar to the one used by the transmitter to synthesize the original signal.
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