References of "Despotovic, Vladimir 50036151"
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See detailAn Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface
Walter, Oliver; Despotovic, Vladimir UL; Haeb-Umbach, Reinhold et al

in Proceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH 2014) (2014, September)

In this paper, we investigate unsupervised acoustic model training approaches for dysarthric-speech recognition. These models are first, frame-based Gaussian posteriorgrams, obtained from Vector ... [more ▼]

In this paper, we investigate unsupervised acoustic model training approaches for dysarthric-speech recognition. These models are first, frame-based Gaussian posteriorgrams, obtained from Vector Quantization (VQ), second, so-called Acoustic Unit Descriptors (AUDs), which are hidden Markov models of phone-like units, that are trained in an unsupervised fashion, and, third, posteriorgrams computed on the AUDs. Experiments were carried out on a database collected from a home automation task and containing nine speakers, of which seven are considered to utter dysarthric speech. All unsupervised modeling approaches delivered significantly better recognition rates than a speaker-independent phoneme recognition baseline, showing the suitability of unsupervised acoustic model training for dysarthric speech. While the AUD models led to the most compact representation of an utterance for the subsequent semantic inference stage, posteriorgram-based representations resulted in higher recognition rates, with the Gaussian posteriorgram achieving the highest slot filling F-score of 97.02%. [less ▲]

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See detailArtificial Intelligence Techniques for Modelling of Temperature in the Metal Cutting Process
Tanikic, Dejan; Despotovic, Vladimir UL

in Metallurgy – Advances in Materials and Processes (2014)

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See detailDesign of nonlinear predictors for adaptive predictive coding of speech signals
Despotovic, Vladimir UL; Peric, Zoran

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 ▲]

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See detailThe Artificial Neural Network Based System for Validation of Thermocouples Used in Biomedicine
Tanikic, Dejan; Despotovic, Vladimir UL; Djenadic, Dalibor et al

in Proceedings of the 13th International Conference on Environment and Electrical Engineering (EEEIC) (2013, November)

Machining operations are widely used in the orthopedic surgery. The temperature which occurs in the cutting zone, during the machining of the bones, may have many negative consequences in the ... [more ▼]

Machining operations are widely used in the orthopedic surgery. The temperature which occurs in the cutting zone, during the machining of the bones, may have many negative consequences in the postoperative period. Therefore, the measuring and the modeling of this parameter is a very important task. In this paper, the thermocouples are presented as a potential tool for the temperature measuring. The paper also deals with the system for validation of the thermocouples. The artificial neural network is used for modeling of the relationship between the electromotive force (as the thermocouple output) and the corresponding temperature. It is shown that the results of the modeling are in good correlation with the measured data. [less ▲]

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See detailLow-Order Volterra Long-Term Predictors
Despotovic, Vladimir UL; Goertz, Norbert; Peric, Zoran

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 ▲]

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See detailImproved Non-Linear Long-Term Predictors based on Volterra Filters
Despotovic, Vladimir UL; Goertz, Norbert; Peric, Zoran

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 ▲]

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See detailNonlinear long-term prediction of speech based on truncated Volterra series
Despotovic, Vladimir UL; Goertz, Norbert; Peric, Zoran

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 ▲]

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See detailHalf a Century of Computing in Copper Mining and Metallurgy Industry in Serbia
Milivojevic, Dragan; Pavlov, Marijana; Despotovic, Vladimir UL et al

in IEEE Annals of the History of Computing (2012), 34(3), 34-43

The Copper Mining and Smelting Complex Bor (RTB Bor) in the Republic of Serbia has a long history of computer control and computer-aided data processing. Working within the confines of the Cold War era ... [more ▼]

The Copper Mining and Smelting Complex Bor (RTB Bor) in the Republic of Serbia has a long history of computer control and computer-aided data processing. Working within the confines of the Cold War era, RTB implemented and developed four generations of computers, becoming a major influence in the IT sector and the primary computer training resource in the region. [less ▲]

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See detailSwitched adaptive quantiser for speech compression based on optimal companding and correlation
Despotovic, Vladimir UL; Peric, Zoran; Velimirovic, Lazar 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 ▲]

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See detailADPCM Using a Second-order Switched Predictor and Adaptive Quantizer
Despotovic, Vladimir UL; Peric, Zoran

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 ▲]

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See detailDPCM with Forward Gain-Adaptive Quantizer and Simple Switched Predictor for High Quality Speech Signals
Despotovic, Vladimir UL; Peric, Zoran; Velimirovic, Zoran 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 ▲]

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See detailIdentification of systems of arbitrary real order: a new method based on systems of fractional order differential equations and orthogonal distance fitting
Skovranek, Tomas; Despotovic, Vladimir UL

in Volume 4: 7th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B and C (2009, September)

A new method for identification of systems of arbitrary real order based on numerical solution of systems of nonlinear fractional order differential equations (FODEs) and orthogonal distance fitting is ... [more ▼]

A new method for identification of systems of arbitrary real order based on numerical solution of systems of nonlinear fractional order differential equations (FODEs) and orthogonal distance fitting is presented. The main idea is to fit experimental or measured data using a solution of a system of fractional differential equations. The parameters of these equations, including the orders of derivatives, are subject to optimization process, where the criterion of optimization is the minimal sum of orthogonal distances of the data points from the fitting line. Once the minimal sum is found, the identified parameters are considered as optimal. The so called orthogonal distance fitting, known also under the names of total least squares or orthogonal regression is naturally used in the fitting criterion, since it is the most suitable tool for fitting lines and surfaces in multidimensional space. The examples illustrating the methods are presented in 2-dimensional and 3-dimensional problems. [less ▲]

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See detailShadows on the Walls: Geometric Interpretation of Fractional Integration
Podlubny, Igor; Despotovic, Vladimir UL; Skovranek, Tomas et al

in Journal of Online Mathematics and its Applications (2007), 7

In 2001/2002, Podlubny suggested a solution to the more than 300-years old problem of geometric interpretation of fractional integration (i.e., integration of an arbitrary real order). His geometric ... [more ▼]

In 2001/2002, Podlubny suggested a solution to the more than 300-years old problem of geometric interpretation of fractional integration (i.e., integration of an arbitrary real order). His geometric interpretation for left-sided and right-sided Riemann-Liouville fractional integrals, and for Riesz potential is given in terms of changing time scale with constant order of integration, and also in a case of varying order of integration with constant time parameter. In this article we present animations of such interpretation. [less ▲]

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