Adaptive quantization is used in many ADPCM systems to improve performance. The authors present an analysis dealing with the effect of adaptive quantizers on the stability of ADPCM systems as compared with ADPCM with nonadaptive quantization. Stability of the system is noted to equate with recovery from encoder internal state discrepancies. A sufficient condition for stability that leads to a relation between adaptation rate and stability is presented. This indicates that a price is paid for adaptation in terms of more stringent restrictions on the predictor to guarantee stability. The theorem further indicates that stability is related to the maximum rate of quantizer step size decrease and can be viewed as a theoretical justification for the shape of the Jayant "one-word memory" multiplier curve for adaptive quantization.>

Discusses the use of a variable bit rate ADPCM system for speech coding. The authors present an ADPCM system, using arithmetic coding and Kalman filtering techniques, that can code signals at very low bit rates, where standard fixed bit rate ADPCM systems fail. The proposed system, at an average bit rate of 16 kbps, produces a reconstructed speech signal that by informal listening tests has been judged to be of significantly higher quality than the LD-CELP 16 kbps speech coding standard. The authors believe that the output quality is comparable to that of LD-CELP at an average rate of around 12 kbps, and the quality at 8 kbps is very promising. Other advantages of the scheme are a reduction in complexity over the LD-CELP standard, and an increase in design flexibility.>

The algorithms for adaptive quantization and adaptive prediction in existing adaptive differential pulse code modulation (ADPCM) are distinct and decoupled. The designs of the two algorithms are based on the assumption that they act independently whereas, due to the feedback configuration, they must interact in some way. The authors present a preliminary investigation of a new design approach where they perform joint adaptation based on a common cost function, considering the overall system. A “backward” adaptive algorithm is presented and applied to a simple example to demonstrate the feasibility of the general approach.

A common nonlinear adaptive quantization problem is posed as a corresponding linear quadratic control problem. Adaptive quantizer algorithms are generated using methods of disturbance rejection in linear systems. Simulation with speech data shows that these algorithms can outperform a previous well-known method. The new approach subsumes and extends previous algorithms in a logical fashion.>

The use of Kalman filtering (KF) techniques in speech coding is investigated. The authors show that the common linear predictor (LP) is a special case of the KF based on an all-pole signal model. They also show that the KF algorithm provides fixed-lag smoothing at no additional complexity. Simulation results reveal that KF based speech coding has significant advantage over the equivalent LP based systems, particularly when used with coarsely quantized measurements.>