Gaussian mixture model (GMM)-based vector quantization of line spectral frequencies (LSFs) has gained wide acceptance in speech coding. In predictive coding of LSFs, the GMM approach utilizing Kalman filtering princip...
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Gaussian mixture model (GMM)-based vector quantization of line spectral frequencies (LSFs) has gained wide acceptance in speech coding. In predictive coding of LSFs, the GMM approach utilizing Kalman filtering principles to account for quantization noise has been shown to perform better than a baseline GMM recursive coder approaches for both clean and packet loss conditions at roughly the same complexity. However, the GMM Kalman based predictive coder was not specifically designed for operation in packet loss conditions. In this paper, we show how an initial GMM Kalman predictive coder can be utilized to obtain a robust GMM predictive coder specifically designed to operate in packet loss. In particular, we demonstrate how one can define sets of encoding and decoding modes, and design special Kalman encoding and decoding gains for each set. With this framework, GMM predictive coding design can be viewed as determining the special Kalman gains that minimize the expected minimum mean squared error at the decoder in packet loss conditions. The simulation results demonstrate that the proposed robust Kalman predictive coder achieves better performance than the baseline GMM predictive coders.
Presents a new image analysis technique for edge detection using multistage predictive coding (MPC). MPC is a progressive data compression technique which decomposes an image into a set of image components in multiple...
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Presents a new image analysis technique for edge detection using multistage predictive coding (MPC). MPC is a progressive data compression technique which decomposes an image into a set of image components in multiple stages from which the original image can be recovered. The proposed coding scheme to implement MPC is multistage delta modulation (MDM) which includes a multistage quantizer for image decomposition and a priority code for image reconstruction. The utilization of the multistage quantizer is to decompose an image into multiple stages progressively in accordance with the significance of the image description. The task of the priority code is to prioritize all stages generated by the multistage quantizer; the lower the stage, the higher the priority. Using the priority code enables to extract features from the most significant edges to the least important details based on decreasing priorities. The experimental results are very encouraging and show that MDM is indeed a promising edge detection and feature extraction technique for real-time processing.< >
A great deal of current research in the area of narrowband digital speech compression makes use of the Linear Prediction coding (LPC) algorithm to extract the vocal track spectrum. This paper describes a technique tha...
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A great deal of current research in the area of narrowband digital speech compression makes use of the Linear Prediction coding (LPC) algorithm to extract the vocal track spectrum. This paper describes a technique that splits the spectrum into two equal halves and performs a piecewise LPC approximation to each half. By taking advantage of the classical benefits of piecewise approximation, the fidelity is expected to be higher than standard LPC. In addition, by making use of under-sampling and spectrum folding, computational requirements are reduced by about 40%. PLPC has been implemented in real time on the CSP-30 computer at the Speech Research and Development Facility of the Communications Security Engineering Office (DCW) at ESD.
This paper presents an algorithm for lossy compression of hyperspectral images for implementation on field programmable gate arrays (FPGA). To greatly reduce the bit rate required to code images, linear prediction is ...
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This paper presents an algorithm for lossy compression of hyperspectral images for implementation on field programmable gate arrays (FPGA). To greatly reduce the bit rate required to code images, linear prediction is used between the bands to exploit the large amount of inter-band correlation. The prediction residual is compressed using the set partitioning in hierarchical trees algorithm. To reduce the complexity of the predictive encoder, this paper proposes a bit plane-synchronized closed loop predictor that does not require full decompression of a previous band at the encoder. The new technique achieves almost the same compression ratio as standard closed loop predictive coding and has a simpler on-board implementation.
Streaming sensorial data poses major computational challenges, such as, lack of storage, inapplicability of offline algorithms, and the necessity to capture nonstationary data distributions with concept drifts. Our go...
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Streaming sensorial data poses major computational challenges, such as, lack of storage, inapplicability of offline algorithms, and the necessity to capture nonstationary data distributions with concept drifts. Our goal is to build a learner framework that uses the current data and the knowledge from historical data to predict the next data in an efficient, unsupervised and online manner. Labeled streaming data is scarce, hence prediction of data instead of labels is a more realistic problem. We present a learner model, called SELP, for learning in variances as features from explanations of surprises due to prediction errors in streaming spatiotemporal data. This model runs a relentless cycle of Surprise → Explain → Learn → Predict involving the real external world and its internal model. The learner is continuously updated, independent of a trigger, proportional to its surprise. It implements a more efficient version of predictive coding, a form of biologically-plausible information coding paradigm, by predicting changes in the data instead of the data itself. Experimental results obtained from deploying our implementation on synthesized and real-world data are qualitatively comparable to that of traditional predictive coding on similar data sets. The results also offer insights into the learner design. This research lays out the foundations for an agent-based framework with an internal model grounded to the data stream.
A major source of audible distortion in current low-bit-rate speech coding algorithms is an inaccurate degree of periodicity of the voiced speech signal. If the correlations between neighboring pitch cycles are accura...
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A major source of audible distortion in current low-bit-rate speech coding algorithms is an inaccurate degree of periodicity of the voiced speech signal. If the correlations between neighboring pitch cycles are accurately reproduced, these audible distortions can be reduced significantly. To this purpose, a novel method of coding voiced speech is introduced, which transmits an encoded prototype waveform at 20-30 ms intervals. The prototype waveform describes a pitch cycle representative for the interval, and is quantized using analysis-by-synthesis methods. The speech signal is reconstructed by concatenation of interpolated prototype waveforms. The short-term and the long-term correlations between pitch cycles can be controlled explicitly. Unquantized reconstructed speech is virtually indistinguishable from the original signal. The method results in excellent speech quality at rates between 3.0 and 4.0 kb/s.< >
predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient huma...
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ISBN:
(纸本)9781467390064
predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient human document review. Previously, the sole front-end input used to create a predictive model was the exemplar documents (training data) chosen by subject-matter experts. Many predictive coding tools require users to rely on static preprocessing parameters and a single machine learning algorithm to develop the predictive model. Little research has been published discussing the impact preprocessing parameters and learning algorithms have on the effectiveness of the technology. A deeper dive into the generation of a predictive model shows that the settings and algorithm can have a strong effect on the accuracy and efficacy of a predictive coding tool. Understanding how these input parameters affect the output will empower legal teams with the information they need to implement predictive coding as efficiently and effectively as possible. This paper outlines different preprocessing parameters and algorithms as applied to multiple real-world data sets to understand the influence of various approaches.
In this paper, an online differential compression algorithm with reset columns integrated along with a digital pixel sensor (DPS) array is proposed. The proposed architecture of the sensor array reduces by more than h...
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In this paper, an online differential compression algorithm with reset columns integrated along with a digital pixel sensor (DPS) array is proposed. The proposed architecture of the sensor array reduces by more than half the silicon area of the DPS by sampling and storing the differential values between the pixel and its prediction, featuring compressed dynamic range and hence requiring limited precision (typically 2-3 bits as compared to 8-bit full precision). Column based reset technique is proposed to overcome the error accumulation problem inherent in predictive coding. While the concept of predictive coding was extensively introduced in previous literature, this is the first time this concept is used to reduce the storage requirement at the pixel level and hence drastically improving both the pixel size and the fill-factor - a key problem in DPS implementation. System level simulation results show the importance of the proposed reset scheme while VLSI implementation results illustrate a pixel level implementation of the whole predictive coding scheme featuring a pixel size reduction of more than 40% with a fill-factor of more than 15%.
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