nils-herrmann/clean_scirepeval huggingface dataset:
Joint optimization of the perceptual core and lossless compression layers in scalable audio coding

MPEG-4 High-Definition Advanced Audio Coding (HD-AAC) enables scalable-to-lossless (SLS) audio coding with an Advanced Audio Coding (AAC) base layer, and fine-grained enhancements based on the MPEG SLS standard. While the AAC core offers better perceptual quality at lossy bit-rates, its inclusion has been observed to compromise the ultimate lossless compression performance as compared to the SLS ‘non-core’ (i.e., without an AAC base layer) codec. In contrast, the latter provides excellent lossless compression but with significantly degraded audio quality at low bit-rates. We propose a trellis-based approach to directly optimize the trade-off between the quality of the AAC core and the lossless compression performance of SLS. Simulations to test the effectiveness of the approach demonstrate the capability to adjust the trade-off to match application specific needs. Moreover, such optimization can in fact achieve an AAC core of superior perceptual quality while maintaining state-of-the-art (and surprisingly sometimes even better) lossless compression, all this in compliance with the HD-AAC standard.
A 12-bit 65 MS/s pipeline A/D converter in 0.18 μm SiGe BiCMOS

This work discusses the benefits of a SiGe BiCMOS implementation over a CMOS implementation for pipeline A/Ds. While various circuit blocks in a pipeline A/D can benefit from the higher transconductance (gm), higher unity gain frequency (fT) and lower noise of SiGe HBTs, this work focuses on the most critical block in a pipeline A/D, the operational transconductance amplifier (OTA). An OTA employing an all NMOS pre-amplifier followed by a cascoded SiGe HBT stage with actively cascoded PMOS loads is designed. A prototype 12-bit pipeline A/D achieves a measured SNDR of 62.6 dB and a SFDR of 73.4 dB at 65 MS/s with a power dissipation of 325 mW and operates from dual 1.8 V and 3.3 V supplies.
Deterministically annealed design of speech recognizers and its performance on isolated letters

We attack the general problem of HMM-based speech recognizer design, and in particular, the problem of isolated letter recognition in the presence of background noise. The standard design method based on maximum likelihood (ML) is known to perform poorly when applied to isolated letter recognition. The minimum classification error (MCE) approach directly targets the ultimate design criterion and offers substantial improvements over the ML method. However, the standard MCE method relies on gradient descent optimization which is susceptible to shallow local minima traps. We propose to overcome this difficulty with a powerful optimization method based on deterministic annealing (DA). The DA method minimizes a randomized MCE cost subject to a constraint on the level of entropy which is gradually relaxed. It may be derived based on information-theoretic or statistical physics principles. DA has a low implementation complexity and outperforms both standard ML and the gradient descent based MCE algorithm by a factor of 1.5 to 2.0 on the benchmark CSLU spoken letter database. Further, the gains are maintained under a variety of background noise conditions.
Historical Relationships Among Fisheries Abundance and Hydrographic and Pollution Variables in Northeastern Estuaries

Categorical time series regression was applied to 24 commercial fisheries in five northeastern estuaries for the period 1929-1977. Explanatory variables included were: relative stock abundance, hydrographic variables, and pollution indicator variables, all lagged back in time to represent the conditions influencing the multiple age classes comprising each fishery. Results showed that interactions among explanatory variables (lagged stock, hydrographic and pollution variables) were very important in controlling year-to-year variation in commercial fisheries abundance.
Do the designs work? (VLSI design education)

The author presents a philosophy of VLSI design education based on four premises: managing a VLSI design requires a hierarchical approach; a library of standard cells simplifies and increases design reliability; VLSI design requires interaction between the designer and a supportive CAE (computer-aided engineering) environment; and to allow reliable design by inexperienced designers, the CAE environment must assure design correctness. Training inexperienced designers requires providing feedback (including grades) on whether or not their designs work. The CAE tools must provide this feedback at all levels of the design process: layout circuit, logic and architecture. Assuring that designs work is a necessary part of design education. To achieve this, feedback to designers is essential at all levels and stages of the design process.<<ETX>>
Hierarchical clustering using deterministic annealing

The authors present a new approach to the problem of hierarchical clustering. The method implements an approximation to joint optimization over all levels of the hierarchy, utilizing deterministic annealing to improve the clustering solution. Similar to the splitting algorithm, cluster nodes at all tree levels are placed at generalized region centroids. In this method, though, the node centroids are updated to explicitly enforce desired classification at the leaves, and to approximate the unconstrained clustering solution. The approach was demonstrated to avoid local minima that trap the splitting algorithm and to obtain a performance improvement for a normal mixture source and a speech source.<<ETX>>
Optimized video streaming over lossy networks with real-time estimation of end-to-end distortion

This paper is concerned with adaptive and robust video streaming over lossy networks. A general system is described which is compatible with virtually all application settings. The delivery procedure is optimized within a rate-distortion framework with the aid of an efficient real-time end-to-end distortion estimation algorithm. The estimate fully accounts for the effects of (prior) quantization, packet loss and error propagation, as well as error concealment. It features high accuracy and low complexity, and requires a small amount of pre-computed side information. It is shown that various error-resilient schemes can be optimized within the proposed framework. In particular, unequal error protection through forward error control is employed in the simulations to demonstrate the achievable performance gains.
The asymptotic closed-loop approach to predictive vector quantizer design with application in video coding

The basic vector quantization (VQ) technique employed in video coding belongs to the category of predictive vector quantization (PVQ), as it involves quantization of the (motion compensated) frame prediction error. It is well known that the design of PVQ suffers from fundamental difficulties, due to the prediction loop, which have an impact on the convergence and the stability of the design procedure. We propose an approach to PVQ design that enjoys the stability of open-loop design while it ensures ultimate optimization of the closed-loop system. The method is derived for general predictive quantization, and we demonstrate it on video compression at low bit rates, where it provides substantial improvement over standard open and closed loop design techniques. Further, the approach outperforms standard DCT-based video coding.
Challenges and Recent Advances in Distributed Predictive Coding

Distributed coding of correlated sources with memory poses a number of considerable challenges that threaten practical applications, particularly (but not only) in the context of sensor networks. This problem is strongly motivated by the obvious observation that most common sources exhibit temporal correlations that may be at least as important as spatial or inter-source correlations. This paper presents an analysis of the underlying tradeoffs, paradigms for coding systems, and approaches for distributed predictive coder design optimization. Motivated by practical limitations on both complexity and delay (especially for dense sensor networks) the focus here is on predictive coding. From the source coding perspective, the most basic tradeoff (and difficulty) is due to conflicts that arise between distributed coding and prediction, wherein 'standard' distributed quantization of the prediction errors, if coupled with imposition of zero decoder drift, would drastically compromise the predictor performance and hence the ability to exploit temporal correlations. Another challenge arises from instabilities in the design of closed loop predictors, whose impact has been observed in the past, but is greatly exacerbated in the case of distributed coding. The main contribution focuses on the tradeoffs encountered within a more general paradigm where decoder drift is allowable or unavoidable, and must be effectively accounted for and controlled. We briefly review our earlier results on which we build to derive an overall design optimization method that avoids the pitfalls of naive distributed predictive quantization and produces an optimized low complexity and low delay coding system.
Robust vector quantizer design by noisy channel relaxation

This article proposes a method to design a vector quantizer (VQ) for robust performance under noisy channel conditions. By re-optimizing the quantizer at progressively lower levels of assumed channel noise, the design is less susceptible to poor local optima. The method is applied to: (1) channel-optimized VQ design; and (2) index assignment for a source-optimized VQ. For both problems, we demonstrate substantial performance improvements over commonly used techniques.
Variable block-size transform image coder

A discrete cosine transform image coding scheme based on adaptive block size is proposed and demonstrated. The scheme adaptively optimizes the block sizes according to some local activity indexes of the image scenes. The optimization is achieved by applying the technique from graph theory. Nine rectangular block sizes are used, corresponding to all combinations of 8, 16, and 32 pixels per side. This approach reduces interblock correlation between coefficients, and increases the energy compaction in the transform domain. Experimental results are presented and compared to results obtained by an identical coder using a fixed block size.
End-to-end distortion estimation for RD-based robust delivery of pre-compressed video

Applications where packetized video is streamed over the Internet, must be designed to achieve robustness to packet loss as well as compression efficiency. Whenever possible, the ideal solution to this problem is to jointly optimize the adaptation of the compression and error protection strategies to the network status, so as to minimize the expected end-to-end distortion of reconstructed video at the receiver. However, in the case of pre-compressed video streaming, compression is performed without knowledge of the network condition, and conversely the delivery is performed without access to the original signal. It is hence difficult for the transmitter to estimate and minimize the end-to-end distortion during delivery. This paper addresses this problem by deriving an algorithm which enables the transmitter, or other intermediate nodes, to estimate the overall end-to-end distortion while delivering pre-compressed video. This estimate fully accounts for the effects of (prior) quantization, packet loss and error propagation, as well as error concealment. The accuracy of the estimate is demonstrated by simulation results. The algorithm requires storage of minimal side-information that is computed during compression. The algorithm is of low complexity, and is applicable to virtually all coding techniques, including the standard (predictive) video coders. The paper also discusses the use of this estimate to adapt a variety of packet-loss resilience techniques for pre-compressed video streaming. The considerable potential gains of this approach are illustrated via the example of an FEC-based streaming video system.
A deterministic annealing approach to Witsenhausen's counterexample

This paper proposes an optimization method, based on information theoretic ideas, to a class of distributed control problems. As a particular test case, the well-known and numerically “over-mined” problem of decentralized control and implicit communication, commonly referred to as Witsenhausen's counterexample, is considered. The key idea is to randomize the zero-delay mappings. which become “soft”, probabilistic mappings to be optimized in a deterministic annealing process, by incorporating a Shannon entropy constraint in the problem formulation. The entropy of the mapping is controlled and gradually lowered to zero to obtain deterministic mappings, while avoiding poor local minima. For the particular test case, our approach obtains new mappings that shed light on the structure of the optimal solution, as well as achieving a small improvement in total cost over the state of the art in numerical approaches to this problem. Proposed method is general and applicable to any problem of similar nature.
Open-loop design of predictive vector quantizers for video coding

The basic vector quantization (VQ) technique employed in video coding belongs to the category of predictive vector quantization (PVQ), as it involves quantization of the (motion compensated) frame prediction error. It is well known that the design of PVQ suffers from fundamental difficulties, due to the prediction loop, which have an impact on the convergence and the stability of the design procedure. We propose an approach to PVQ design that enjoys the stability of open-loop design while it ensures ultimate optimization of the closed-loop system. The method is derived for general predictive quantization, and we demonstrate it on video compression at low bit rates, where it provides substantial improvement over standard open and closed loop design techniques.
A deterministic annealing approach to discriminative hidden Markov model design

We present the problem of designing a classifier system based on hidden Markov models (HMMs) from a labeled training set with the objective of minimizing the rate of misclassification. To design the globally optimal recognizer, all the HMMs must be jointly optimized to minimize the number of mis-classified training patterns. This is a difficult design problem which we attack using the technique of deterministic annealing (DA). In the DA approach, we introduce randomness in the classification rule and minimize the expected mis-classification rate of the random classifier while controlling the level of randomness in its decision via a constraint on the Shannon entropy. The effective cost function is smooth and converges to the mis-classification cost at the limit of zero entropy (non-random classification rule). The DA approach can be implemented via an efficient forward-backward algorithm for recomputing the model parameters. This algorithm significantly outperforms the standard maximum likelihood algorithm for a moderate increase in design complexity.
Perceptual harmonic cepstral coefficients for speech recognition in noisy environment

Perceptual harmonic cepstral coefficients (PHCC) are proposed as features to extract from speech for recognition in noisy environments. A weighting function, which depends on the prominence of the harmonic structure, is applied to the power spectrum to ensure accurate representation of the voiced speech spectral envelope. The harmonics' weighted power spectrum undergoes mel-scaled band-pass filtering, and the log-energy of the filters' output is discrete cosine transformed to produce cepstral coefficients. Lower spectral clipping is applied to the power spectrum, followed by within-filter root-power amplitude compression to reduce amplitude variation without compromise of the gain invariance properties. Experiments show significant recognition gains of PHCC over MFCC, with 23% and 36% error rate reduction for the Mandarin digit database in white and babble noise environments.
An information theoretic model for adaptive lossy compression

We investigate the underlying mechanism of universal sequential, fixed distortion, lossy source coding algorithms, through the "eyes" of a random code. We model the adaptive codebook as a "mixed type" random code whose codeword type distribution evolves with time while its dimension goes to infinity. The evolution law in our model has a structure of a tree; a type inherits its frequency in the code from its parent-types matching probabilities. We find that this mechanism naturally selects the "good" codeword types for compressing the (unknown) source. As a consequence, the code becomes optimal for the source, and approaches the rate-distortion function as the time, and hence the dimension, go to infinity. Beyond its analytical interest, this model also provides guidelines for developing lossy string matching (Lempel-Ziv-like) algorithms.
Linearity conditions for optimal estimation from multiple noisy measurements

Source estimation from several noisy observations, each of which are corrupted with additive independent noise is arguably among the most fundamental problems in estimation theory. Estimating multiple independent sources, corrupted by identical noise realization is important in various communication applications. It is well-known for both cases that when all sources and noises are Gaussian, linear estimator minimizes the mean square estimation error. This paper analyzes the conditions for linearity of optimal estimation in these two settings, for general source and noise distributions and distortion measures. Specifically, we show that these settings depart from the single source-single channel setting in that Gaussianity of all system components is necessary to render the Lp optimal estimator linear at a given signal-to-noise ratio (SNR). Moreover, we show for both settings that at asymptotically high SNR, for Gaussian sources the optimal estimator converges to linear, irrespective of the distribution of the noises; similarly, at low SNR, it is asymptotically linear for Gaussian noises regardless of the sources.
Distributed Multi-stage Coding of Correlated Sources

This paper considers the problem of distributed scalable coding of correlated sources that are communicated to a central unit. The general setting is typically encountered in sensor networks. The conditions of communication channels between the sensor sources and fusion center may be time-varying and it is often desirable to guarantee a base layer of coarse information during channel fades. Specifically, we consider a multi-stage coding system to perform such distributed scalable coding of correlated sources. This problem poses new challenges. We show that mere extensions of distributed coding ideas to include multi-stage coding yield poor rate-distortion performance, due to underlying conflicts between the objectives of scalable and distributed coding. An appropriate system paradigm is developed which allows such tradeoffs to be explicitly controlled within joint optimization of all the system components. We propose an iterative joint design technique and derive the necessary conditions for optimality which yield its update rules. Simulation results show substantial gains over single source (separate) multi-stage coding as well as naive extensions to incorporate scalability in distributed scalable coding schemes.
An estimation-theoretic framework for spatially scalable video coding with delayed prediction

A novel estimation-theoretic (ET) approach is developed for optimal enhancement layer prediction, in spatially scalable video coding (SVC), which incorporates motion compensation at the enhancement layer, with both current and future information from the base layer. It is inspired by the early ET framework (originated in our group) for quality (SNR) scalability, which achieved optimal enhancement layer prediction by fully accounting for information from the current base layer (e.g., the quantization intervals) and the enhancement layer, to efficiently calculate the conditional expectation that forms the optimal predictor. Central to that approach was the fact that all layers reconstruct approximations to the same original transform coefficient. This, however, is not the case in spatial scalability, where the layers encode different resolution versions of the signal. To approach optimal enhancement layer prediction, the current work departs from existing spatial SVC schemes that employ pixel-domain resampling and causal prediction. Instead, it integrates a transform domain resampling technique that makes the base layer quantization intervals and reconstructions accessible to and usable at the enhancement layer. The approach is extended for an SVC framework that allows delay in enhancement layer coding relative to the base layer, and achieves optimal delayed prediction, in conjunction with spatial SVC. Simulations provide experimental evidence that the overall proposed approach substantially outperforms existing spatially scalable coders.
A two-layered trellis approach to audio encoding

The fact that audio compression for streaming or storage is usually performed offline alleviates traditional constraints on encoding delay. We propose a rate-distortion optimized approach, within the MPEG Advanced Audio Coding framework, to trade delay for optimal window switching, resource allocation and selection of quantization and coding parameters for the entire audio file using a two-layered trellis. Stages of the outer trellis correspond to audio frames, nodes represent window choices, and branches implement transition constraints. The inner trellis operates within each node of the outer layer and has stages corresponding to scalefactor bands and nodes representing combinations of quantization and coding parameters. A suitable cost, comprising bit consumption and psychoacoustic distortion, is optimized via multiple passes through the two-layered trellis to achieve the desired bitrate. The procedure thus optimizes most of the encoding decisions involved in audio compression. Objective and subjective tests show considerable performance gains.
A conditional enhancement-layer quantizer for the scalable MPEG advanced Audio Coder

We propose an efficient enhancement-layer quantizer which considerably improves the bit rate scalability of the multi-layer Advanced Audio Coder (AAC). The scheme exploits the statistical dependence of the enhancement-layer signal on the base-layer quantization parameters. It fundamentally extends the prior work on compander domain scalability, which was shown to be asymptotically optimal for entropy coded uniform scalar quantizer, to systems with non-uniform base-layer quantization. We show that an enhancement-layer quantization which is conditional on the base-layer information can be efficiently implemented within the AAC framework to achieve major performance gains. Moreover, in the important case that the source is well modeled as Laplacian, we show that the optimal conditional quantizer is implementable by only two distinct switchable quantizers depending on whether or not the base-layer quantizer employed the “zero dead-zone.” Hence, major savings in bit rate are recouped at virtually no additional computational cost. For example, the proposed four layer scalable coder consisting of 16kbps layers achieves performance close to a 60kbps non-scalable coder on the standard test database of 44.1kHz audio.