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Delta Modulation (DM) is a simple waveform coding algorithm used mostly when timely data delivery is more important than the transmitted data quality. While the implementation of DM is fairly simple and inexpensive, it suffers from several limitations, such as slope overload and granular noise, which can be overcome using Adaptive Delta Modulation (ADM). This paper presents novel 2-digit ADM with six-level quantization using variable-length coding, for encoding the time-varying signals modelled by Laplacian distribution. Two variants of quantizer are employed, distortion-constrained quantizer which is optimally designed for minimal mean-squared error (MSE), and rate-constrained quantizer, which is suboptimal in the minimal MSE sense, but enables minimal loss in SQNR for the target bit rate. Experimental results using real speech signal are provided, indicating that the proposed configuration outperforms the baseline ADM algorithms, including Constant Factor Delta Modulation (CFDM), Continuously Variable Slope Delta Modulation (CVSDM), 2-digit and 2-bit ADM, and operates in a much wider dynamic range. [ABSTRACT FROM AUTHOR]

Linear prediction is extensively used in modeling, compression, coding, and generation of speech signal. Various formulations of linear prediction are available, both in time and frequency domain, which start from different assumptions but result in the same solution. In this letter, we propose a novel, generalized formulation of the optimal low-order linear prediction using the fractional (non-integer) derivatives. The proposed fractional derivative formulation allows for the definition of predictor with versatile behavior based on the order of fractional derivative. We derive the closed-form expressions of the optimal fractional linear predictor with restricted memory, and prove that the optimal first-order and the optimal second-order linear predictors are only its special cases. Furthermore, we empirically prove that the optimal order of fractional derivative can be approximated by the inverse of the predictor memory, and thus, it is a priori known. Therefore, the complexity is reduced by optimizing and transferring only one predictor coefficient, i.e., one parameter less in comparison to the second-order linear predictor, at the same level of performance. [ABSTRACT FROM AUTHOR]

This paper introduces two novel algorithms for the 2-bit adaptive delta modulation, namely 2-bit hybrid adaptive delta modulation and 2-bit optimal adaptive delta modulation. In 2-bit hybrid adaptive delta modulation, the adaptation is performed both at the frame level and the sample level, where the estimated variance is used to determine the initial quantization step size. In the latter algorithm, the estimated variance is used to scale the quantizer codebook optimally designed assuming Laplace distribution of the input signal. The algorithms are tested using speech signal and compared to constant factor delta modulation, continuously variable slope delta modulation and instantaneously adaptive 2-bit delta modulation, showing that the proposed algorithms offer higher performance and significantly wider dynamic range. [ABSTRACT FROM AUTHOR]

An adaptive three-level delta modulation with a switched second-order linear prediction is proposed in this paper, intended for encoding the time-varying signals modeled by Gaussian distribution. The input signal is processed frame-by-frame, and the adaptation of the quantizer is performed at the frame level. The signal at the output of quantizer is further processed using variable length encoder to decrease the bit rate. The performance is tested in speech coding, showing that the proposed algorithm provides much wider dynamic range and attains higher Signal to Noise Ratio with respect to the baselines, including CFDM, CVSDM and 2-bit Adaptive Delta Modulation. [ABSTRACT FROM AUTHOR]

In this paper a delta modulation speech coding scheme based on the ITU-T G.711 standard and the switched first-order predictor is presented. The forward adaptive scheme is used, where the adaptation to the signal variance is performed on frame-by-frame basis. The classification of the frames into weakly and highly correlated was done based on the correlation coefficient calculated for each frame, providing a basis for choosing the appropriate predictor coefficient. The obtained results indicate that the proposed model significantly outperforms the scalar companding system based on the G.711 standard. The obtained experimental results were verified using the theoretical model in the wide dynamic range of the input variance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel solution for coding of time varying signals with Laplacian distribution, which is based on delta modulation and three-level quantization. It upgrades the conventional scheme by introducing the quantizer with variable length code. Forward adaptive scheme is used, where the adaptation to the signal variance is performed on frame-by-frame basis. We employ configurations with simple fixed first-order predictor and switched first-order predictor utilizing correlation. Furthermore, we propose different methods for optimizing predictor coefficients. The configurations are tested on speech signal and compared to an adaptive two-level and four-level delta modulation, showing that proposed three-level delta modulation offers performance comparable to a four-level baseline with significant savings in bit rate. [ABSTRACT FROM AUTHOR]