Kalman Backward Adaptive Predictor Coefficient Identification in ADPCM with PCQ

Kalman backward adaptive predictor coefficient identification is combined with a modified pitch-compensating quantizer (MPCQ) to produce a high-performance adaptive differential pulse code modulation (ADPCM) system for operation at data rates of 12-16 kbits/s. The Kalman/MPCQ system is compared to an ADPCM system using a Kalman algorithm and robust Jayant qnantization and to a system with a fixed-tap predictor and MPCQ. The performance indicators are signal-to-quantization noise ratio (SNR), sound spectrogram analyses, and formal subjective listening tests. The SNR comparisons indicate that the Kalman/ MPCQ system has the highest SNR, followed by the fixed-tap/MPCQ system, and then the Kalman/robust Jayant system. Subjective listening test results show that the Kalman/MPCQ system is preferred over the fixed-tap/MPCQ system 100 percent of the time and over the Kalman/ robust Jayant system 80 percent of the time. Kalman adaptation thus provides an important perceptual effect not evident in the SNR's. The previously catastrophic effects of transmission errors on backward adaptive prediction are eliminated by simple ADPCM system modifications that do not affect the SNR or subjective quality of the output in the absence of errors for the five sentences studied. The problem of tandeming with a linear predictive coder (LPC) is investigated by using LPC processed speech as input to the three ADPCM systems and by using the output of the three ADPCM systems as input to an LPC analysis algorithm. For the LPC to ADPCM connection, the two systems with the MPCQ produce good quality output speech, while the system with robust Jayant quantization exhibits a fading phenomenon. For the ADPCM into LPC analysis, all three systems produce speech of approximately the same quality, with the fixedtap system being slightly, noisier. Using a distance measure proposed by Itakura, the predictor coefficients computed from the three ADPCM system outputs are compared with the predictor coefficients calculated from the uncontaminated speech. According to this distance measure, the coefficients computed from the Kalman/MPCQ system output are much closer to the desired coefficients than are those computed by the other two systems.