A novel paradigm based on pitch-adaptive windows is proposed for solving the problem of encoding the fixed codebook excitation in low bit-rate CELP coders. In this method, the nonzero excitation in the fixed codebook is substantially localized to a set of time intervals called windows. The positions of the windows are adaptive to the pitch peaks in the linear prediction residual signal. Thus, high coding efficiency is achieved by allocating most of the available FCB bits to the perceptually important segments of the excitation signal. The pitch-adaptive method is adopted in the design of a novel multimode variable-rate speech coder applicable to CDMA-based cellular telephony. Results demonstrate that the adaptive windows method yields excellent voice quality and intelligibility at average bit-rates in the range of 2.5-4.0 kbps.

The paper investigates how channel optimization techniques can be applied to predictive vector quantizers. In particular, an efficient encoder search procedure and two design methods are derived. The design methods proposed here, one sample iterative and one block iterative, simultaneously optimize the predictor and the codebook. Extensive simulations show the advantage of this quantization method compared to other memory based quantization schemes as well as memoryless VQ. We also demonstrate that the design methods can be used to obtain index assignments that are advantageous to that obtained by post process index assignment algorithms.

Combined source-channel coding is considered for the case of predictive vector quantization. A design algorithm for channel optimized predictive vector quantizers is proposed. Under reasonable assumptions, the optimal encoder is presented and a sample iterative design method that simultaneously optimizes the predictor and the codebook is derived. We also demonstrate that this design method can be used to obtain index assignments that are advantageous to what is obtained by post process index assignment algorithms. Results are presented for a correlated Gauss-Markov process and for speech LSF parameters.

In linear predictive speech coding algorithms, transmission of linear predictive coding (LPC) parameters-often transformed to the line spectrum frequencies (LSF) representation-consumes a large part of the total bit rate of the coder. Typically, the LSF parameters are highly correlated from one frame to the next, and a considerable reduction in bit rate can be achieved by exploiting this interframe correlation. However, interframe coding leads to error propagation if the channel is noisy, which possibly cancels the achievable gain. In this paper, several algorithms for exploiting interframe correlation of LSF parameters are compared. Especially, performance for transmission over noisy channels is examined, and methods to improve noisy channel performance are proposed. By combining an interframe quantizer and a memoryless "safety-net" quantizer, we demonstrate that the advantages of both quantization strategies can be utilized, and the performance for both noiseless and noisy channels improves. The results indicate that the best interframe method performs as good as a memoryless quantizing scheme, with 4 bits less per frame. Subjective listening tests have been employed that verify the results from the objective measurements.

In this paper, the performance of different predictive vector quantization (PVQ) structures is studied and compared for different degrees of channel noise. Predictive quantization schemes with an auto-regressive (AR) decoder structure are compared with schemes that employ a moving average (MA) decoder. For noisy channels MA prediction performs better than AR. It is shown here that a combination of a PVQ scheme (AR or MA) and a memoryless VQ outperforms both types of traditional predictive quantizer schemes in noiseless as well as noisy channels.

This paper addresses the problem of efficient transmission of the LSF parameters in speech coding using vector quantization (VQ). By performing a comparison of several memory VQ methods on the same database, we investigate what gains can be achieved by exploiting interframe correlation. The memory VQ methods studied are finite-state VQ and linear predictive VQ. By combining the memory VQ with a fixed memoryless VQ, called the safety-net, further improvements in performance can be obtained. It is found that memory VQ can improve the performance with 3-5 bits compared to memoryless VQ for error-free transmission. The best method in this study is a safety-net extended predictive VQ. For noisy channels, most memory methods perform worse than memoryless VQ, but the safety-net predictive VQ outperforms memoryless VQ for all tested channel error rates, with 4 bits less.

An efficient codebook driven voiced excitation coding method producing natural sounding speech is proposed. It can be incorporated as an essential part in a complete speech coder working at low bit rates. The inter-pulse correlation of such a coding scheme is investigated and exploited using linear predictive vector quantization and finite state vector quantization (FSVQ). A new and more robust FSVQ method that is able to more efficiently exploit this correlation is proposed. The new method dynamically combines two memoryless vector quantizers. This dynamic combination method is not restricted to pulse codebooks but can be employed for any coding scheme with inter vector correlation. 1. INTRODUCTION Most modern speech coders are based on the source-filter concept which tries to separate the excitation source waveform from the spectral characteristics of the vocal tract. For voiced speech, the excitation waveform essentially describes the airflow through the vocal folds. For coders op...