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Algorithm based on 2‐bit adaptive delta modulation and fractional linear prediction for Gaussian source coding ; ; Despotovic, Vladimir in IET Signal Processing (2021), 15(6), 410-423 A novel 2-bit adaptive delta modulation (ADM) algorithm is presented based on uniform scalar quantization and fractional linear prediction (FLP) for encoding the signals modelled by a Gaussian probability ... [more ▼] A novel 2-bit adaptive delta modulation (ADM) algorithm is presented based on uniform scalar quantization and fractional linear prediction (FLP) for encoding the signals modelled by a Gaussian probability density function. The study focusses on two major areas: realization of a 2-bit adaptive quantizer based on Q-function approximation that significantly facilitates quantizer design; and implementation of a recently introduced FLP approach with the memory of two samples, which replaces the first-order linear prediction used in standard ADM algorithms and enables improved performance without increasing transmission costs. It furthermore represents the first implementation of FLP in signal encoding, therefore confirming its applicability in a real signal-processing scenario. Based on the performance analysis conducted on a real speech signal, the proposed ADM algorithm with FLP is demonstrated to outperform other 2-bit ADM baselines by a large margin for the gain in signal-to-noise ratio achieved over a wide dynamic range of input signals. The results of this research indicate that ADM with adaptive quantization based on Q-function approximation and adaptive FLP represents a promising solution for encoding/compression of correlated time-varying signals following the Gaussian distribution. [less ▲] Detailed reference viewed: 59 (0 UL)Design of a 2-Bit Neural Network Quantizer for Laplacian Source ; ; et al in Entropy (2021), 23(8), 933 Achieving real-time inference is one of the major issues in contemporary neural network applications, as complex algorithms are frequently being deployed to mobile devices that have constrained storage ... [more ▼] Achieving real-time inference is one of the major issues in contemporary neural network applications, as complex algorithms are frequently being deployed to mobile devices that have constrained storage and computing power. Moving from a full-precision neural network model to a lower representation by applying quantization techniques is a popular approach to facilitate this issue. Here, we analyze in detail and design a 2-bit uniform quantization model for Laplacian source due to its significance in terms of implementation simplicity, which further leads to a shorter processing time and faster inference. The results show that it is possible to achieve high classification accuracy (more than 96% in the case of MLP and more than 98% in the case of CNN) by implementing the proposed model, which is competitive to the performance of the other quantization solutions with almost optimal precision. [less ▲] Detailed reference viewed: 55 (1 UL)Gaussian source coding based on variance-mismatched three-level scalar quantisation using Q-function approximations ; ; Despotovic, Vladimir in IET Communications (2020), 14(4), 594-602 Detailed reference viewed: 102 (4 UL)Two-dimensional fractional linear prediction ; Despotovic, Vladimir ; in Computers and Electrical Engineering (2019), 77 Linear prediction (LP) has been applied with great success in coding of one-dimensional, time-varying signals, such as speech or biomedical signals. In case of two-dimensional signal representation (e.g ... [more ▼] Linear prediction (LP) has been applied with great success in coding of one-dimensional, time-varying signals, such as speech or biomedical signals. In case of two-dimensional signal representation (e.g. images) the model can be extended by applying one-dimensional LP along two space directions (2D LP). Fractional linear prediction (FLP) is a generalisation of standard LP using the derivatives of non-integer (arbitrary real) order. While FLP was successfully applied to one-dimensional signals, there are no reported implementations in multidimensional space. In this paper two variants of two-dimensional FLP (2D FLP) are proposed and optimal predictor coefficients are derived. The experiments using various grayscale images confirm that the proposed 2D FLP models are able to achieve comparable performance in comparison to 2D LP using the same support region of the predictor, but with one predictor coefficient less, enabling potential compression. [less ▲] Detailed reference viewed: 75 (9 UL)Novel Two-Bit Adaptive Delta Modulation Algorithms ; ; Despotovic, Vladimir in Informatica (2019), 30(1), 117-134 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 ... [more ▼] 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. [less ▲] Detailed reference viewed: 127 (1 UL)Optimal fractional linear prediction with restricted memory ; Despotovic, Vladimir ; in IEEE Signal Processing Letters (2019), 26(5), 760-764 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 ... [more ▼] 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. [less ▲] Detailed reference viewed: 82 (7 UL)An efficient two-digit adaptive delta modulation for Laplacian source coding ; ; Despotovic, Vladimir in International Journal of Electronics (2019), 106(7), 1085-1100 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 ... [more ▼] 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. [less ▲] Detailed reference viewed: 89 (3 UL)One-parameter fractional linear prediction Despotovic, Vladimir ; ; in Computers and Electrical Engineering (2018), 69 The one-parameter fractional linear prediction (FLP) is presented and the closed-form expressions for the evaluation of FLP coefficients are derived. Contrary to the classical first-order linear ... [more ▼] The one-parameter fractional linear prediction (FLP) is presented and the closed-form expressions for the evaluation of FLP coefficients are derived. Contrary to the classical first-order linear prediction (LP) that uses one previous sample and one predictor coefficient, the one-parameter FLP model is derived using the memory of two, three or four samples, while not increasing the number of predictor coefficients. The first-order LP is only a special case of the proposed one-parameter FLP when the order of fractional derivative tends to zero. Based on the numerical experiments using test signals (sine test waves), and real-data signals (speech and electrocardiogram), the hypothesis for estimating the fractional derivative order used in the model is given. The one-parameter FLP outperforms the classical first-order LP in terms of the prediction gain, having comparable performance with the second-order LP, although using one predictor coefficient less. [less ▲] Detailed reference viewed: 76 (2 UL)Forward Adaptive Laplacian Source Coding Based on Restricted Quantization ; ; Despotovic, Vladimir et al in Information Technology and Control (2018), 47(2), 209-219 A novel solution for Laplacian source coding based on three-level quantization is proposed in this paper. The restricted three-level quantizer is designed by assuming the restricted Laplacian distribution ... [more ▼] A novel solution for Laplacian source coding based on three-level quantization is proposed in this paper. The restricted three-level quantizer is designed by assuming the restricted Laplacian distribution of the input signal. Quantizer and Huffman encoder are jointly designed. Forward adaptive scheme was employed, where the adaptation to the signal variance (power) was performed on frame-by frame basis. We employ switched model that consists of two restricted quantizers having unequal support regions. The simulation results (measured as SQNR) of the proposed scheme with a switched restricted three-level quantizer are compared to the cases when it involves three-level unrestricted quantizer and the Lloyd-Max quantizers having N=2 and N=4 levels. It is shown that the proposed solution offers performance comparable to the one of N=4 levels Lloyd-Max’s baseline with large savings in bit rate, while outperforming two other baselines. [less ▲] Detailed reference viewed: 92 (0 UL)Dual-mode quasi-logarithmic quantizer with embedded G.711 codec ; ; Despotovic, Vladimir et al in Journal of Electrical Engineering (2018), 69(1), 46-51 The G.711 codec has been accepted as a standard for high quality coding in many applications. A dual-mode quantizer, which combines the nonlinear logarithmic quantizer for restricted input signals and G ... [more ▼] The G.711 codec has been accepted as a standard for high quality coding in many applications. A dual-mode quantizer, which combines the nonlinear logarithmic quantizer for restricted input signals and G.711 quantizer for unrestricted input signals is proposed in this paper. The parameters of the proposed quantizer are optimized, where the minimal distortion is used as the criterion. It is shown that the optimized version of the proposed quantizer provides 5.4 dB higher SQNR (Signal to Quantization Noise Ratio) compared to G.711 quantizer, or equivalently it performs savings in the bit rate of approximately 0.9 bit/sample for the same signal quality. Although the complexity is slightly increased, we believe that due to the superior performance it can be successfully implemented for high-quality quantization. [less ▲] Detailed reference viewed: 82 (3 UL)Design of nonlinear predictors for adaptive predictive coding of speech signals Despotovic, Vladimir ; in Proceedings of the 21st Telecommunications Forum Telfor (TELFOR) (2013, November) Linear predictive coding is probably the most frequently used technique in speech signal processing. Its main advantage comes from the analogy of the simplified vocal tract model with speech production ... [more ▼] Linear predictive coding is probably the most frequently used technique in speech signal processing. Its main advantage comes from the analogy of the simplified vocal tract model with speech production system. However, this neglects nonlinearities in the speech production process. The paper deals with nonlinear prediction of speech based on truncated Volterra series. Long-term one-tap Volterra predictor is designed in order to decrease computational complexity. Further improvements are obtained using frame/subframe structure and fractional delay. [less ▲] Detailed reference viewed: 111 (0 UL)Improved Non-Linear Long-Term Predictors based on Volterra Filters Despotovic, Vladimir ; ; in Proceedings ELMAR-2012 (2012, September) Speech prediction is extensively based on linear models. However, components generated by nonlinear effects are also contained in speech signals, which is neglected using linear techniques. This paper ... [more ▼] Speech prediction is extensively based on linear models. However, components generated by nonlinear effects are also contained in speech signals, which is neglected using linear techniques. This paper presents long-term nonlinear predictor based on second-order Volterra filters that is shown to be superior to linear long-term predictor with only a minimal increase in complexity and the number of coefficients. It can be used connected in cascade with short-term linear predictor. The frame/subframe structure is proposed, where each frame is divided into four subframes. Second order Volterra long-term prediction is applied to each subframe separately. [less ▲] Detailed reference viewed: 29 (0 UL)Low-Order Volterra Long-Term Predictors Despotovic, Vladimir ; ; in Proceedings of the 10. ITG Symposium on Speech Communication (2012, September) Models based on linear prediction have been used for several decades in different areas of speech signal processing. While the linear approach has led to great advances in the last 40 years, it neglects ... [more ▼] Models based on linear prediction have been used for several decades in different areas of speech signal processing. While the linear approach has led to great advances in the last 40 years, it neglects nonlinearities present in the speech production mechanism. This paper compares the results of long-term nonlinear prediction based on second-order and third-order Volterra filters. Additional improvement can be obtained using fractionaldelay long-term prediction. Experimental results reveal that the proposed method outperforms linear long-term prediction techniques in terms of prediction gain. [less ▲] Detailed reference viewed: 25 (0 UL)Nonlinear long-term prediction of speech based on truncated Volterra series Despotovic, Vladimir ; ; in IEEE Transactions on Audio, Speech and Language Processing (2012), 20(3), 1069-1073 Previous studies of nonlinear prediction of speech have been mostly focused on short-term prediction. This paper presents long-term nonlinear prediction based on second-order Volterra filters. It will be ... [more ▼] Previous studies of nonlinear prediction of speech have been mostly focused on short-term prediction. This paper presents long-term nonlinear prediction based on second-order Volterra filters. It will be shown that the presented predictor can outperform conventional linear prediction techniques in terms of prediction gain and “whiter” residuals. [less ▲] Detailed reference viewed: 136 (0 UL)Switched adaptive quantiser for speech compression based on optimal companding and correlation Despotovic, Vladimir ; ; et al in IET Signal Processing (2011), 5(7), 701-707 This study describes a novel adaptive quantiser based on the optimal companding technique. Adaptation is achieved by adjusting the input of the fixed or non-adaptive quantiser according to the estimated ... [more ▼] This study describes a novel adaptive quantiser based on the optimal companding technique. Adaptation is achieved by adjusting the input of the fixed or non-adaptive quantiser according to the estimated and quantised gain on each particular frame. In such a way better quantiser adaptation to the varying input statistics is provided. Selection of the appropriate bit rate is performed depending on the value of the correlation coefficient ρ on each frame. The decision thresholds for ρ are determined under the condition that the signal to quantisation noise ratio does not drop under 34.3ρdB, satisfying the G.712 standard quality of speech, while decreasing the bit rate. The information about the gain and about the chosen bit rate is then transferred as a side information to a decoder. Although this slightly increases the side information, the overall savings in the bit rate have shown to be substantial. Theoretical and experimental results are provided, which point out the benefits that can be achieved using the proposed algorithm. [less ▲] Detailed reference viewed: 92 (0 UL)ADPCM Using a Second-order Switched Predictor and Adaptive Quantizer Despotovic, Vladimir ; in Advances in Electrical and Computer Engineering (2011), 11(3), 61-64 Adaptive differential pulse code modulation (ADPCM) with forward gain-adaptive quantizer and second-order switched predictor based on correlation is presented in this article. Predictor consists of a bank ... [more ▼] Adaptive differential pulse code modulation (ADPCM) with forward gain-adaptive quantizer and second-order switched predictor based on correlation is presented in this article. Predictor consists of a bank of predetermined predictors for each block of speech samples, avoiding the need to solve, or quantize predictor coefficients during the coding process. The adaptation consists of switching to one of this predictors based on the values of the first and second order correlation coefficients. The theoretical model is generalization of the DPCM with the first order switched predictor for an arbitrary prediction order. Experimental results for ADPCM with the second-order four/eight state switched prediction based on correlation are provided. [less ▲] Detailed reference viewed: 119 (0 UL)DPCM with Forward Gain-Adaptive Quantizer and Simple Switched Predictor for High Quality Speech Signals Despotovic, Vladimir ; ; et al in Advances in Electrical and Computer Engineering (2010), 10(4), 95-98 In this article DPCM (Differential Pulse Code Modulation) speech coding scheme with a simple switched first order predictor is presented. Adaptation of the quantizer to the signal variance is performed ... [more ▼] In this article DPCM (Differential Pulse Code Modulation) speech coding scheme with a simple switched first order predictor is presented. Adaptation of the quantizer to the signal variance is performed for each particular frame. Each frame is classified as high or low correlated, based on the value of the correlation coefficient, then the selection of the appropriate predictor coefficient and bitrate is performed. Low correlated frames are encoded with a higher bitrate, while high correlated frames are encoded with a lower bitrate without the objectionable loss in quality. Theoretical model and experimental results are provided for the proposed algorithm. [less ▲] Detailed reference viewed: 79 (0 UL) |
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