deliver the same high-speech quality in 4 bits per symbol that takes 8 bits per symbol with u-law or A-law PCM? The extra 4 bits worth of informa- tion is coming from the intelligence built into the adaptive predictor. If the predictor Inverse ADPCM Adaptive Linear PCM 15-Level Quantizer Adaptive Predictor Figure 18.7 ADPCM decoder. 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. Speech Coding Speech Coding Adaptive Linear PCM 15-Level ADPCM Quantizer Inverse Adaptive Adaptive Predictor 15-Level Quantizer Figure 18.8 ADPCM encoder. does a good job, then the L - P terms will stay small, and 15 levels of difference will cover almost every sample. ADPCM relies on the adaptive predictor's ability to antici- pate the next sample. The intelligence used in ADPCM is the result of study of the typical variations over time of the frequency and volume of human speech. As a result, ADPCM can create sound quality that is very close to u-law PCM using half the bandwidth. However, ADPCM would not be best for modem or fax transmissions because these signals are quite different from human speech patterns. 8 18.2 Linear Predictive Coding The PCM schemes operate in the time domain. The next steps in speech coding effi- ciency take us out of the time domain into the frequency domain. Speech waveforms are decomposed into their component frequencies and amplitudes, as we discussed in Sec. 1.2. By modeling speech as a frequency spectrum varying over time, we can use the redundancy of human speech audio signals to send speech with a bit rate that is lower than ADPCM. 18.2.1 Code-excited linear prediction (CELP) The CELP speech coder used in one form or another in all our CDMA wireless tele- phone systems uses a table of speech sounds called a codebook. The decoder looks up the codebook index I and multiplies it by a loudness-level factor G to get a basic sound. Thi