nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic 3-D Visualization of Vocal Tract Shaping During Speech

Noninvasive imaging is widely used in speech research as a means to investigate the shaping and dynamics of the vocal tract during speech production. 3-D dynamic MRI would be a major advance, as it would provide 3-D dynamic visualization of the entire vocal tract. We present a novel method for the creation of 3-D dynamic movies of vocal tract shaping based on the acquisition of 2-D dynamic data from parallel slices and temporal alignment of the image sequences using audio information. Multiple sagittal 2-D real-time movies with synchronized audio recordings are acquired for English vowel-consonant-vowel stimuli /ala/, /ara/, /asa/, and /a∫a/. Audio data are aligned using mel-frequency cepstral coefficients (MFCC) extracted from windowed intervals of the speech signal. Sagittal image sequences acquired from all slices are then aligned using dynamic time warping (DTW). The aligned image sequences enable dynamic 3-D visualization by creating synthesized movies of the moving airway in the coronal planes, visualizing desired tissue surfaces and tube-shaped vocal tract airway after manual segmentation of targeted articulators and smoothing. The resulting volumes allow for dynamic 3-D visualization of salient aspects of lingual articulation, including the formation of tongue grooves and sublingual cavities, with a temporal resolution of 78 ms.
A top-down auditory attention model for learning task dependent influences on prominence detection in speech

A top-down task-dependent model guides attention to likely target locations in cluttered scenes. Here, a novel biologically plausible top-down auditory attention model is presented to model such task-dependent influences on a given task. First, multi-scale features are extracted based on the processing stages in the central auditory system, and converted to low-level auditory "gist" features. These features capture rough information about the overall scene. Then, the top-down model learns the mapping between auditory gist features and the scene categories. The proposed top-down attention model is tested with prominent syllable detection task in speech. When tested on broadcast news-style read speech using the BU Radio News Corpus, the model achieves 85.8% prominence detection accuracy at syllable level. The results compare well to the reported human performance on this task.
Approaching optimality in spatially scalable video coding: From resampling and prediction to quantization and entropy coding

This paper builds on our recent work on optimal prediction in spatially scalable video coding, and is inspired by earlier work in our lab on optimal approaches for quality (or SNR) scalability. The approach we propose herein complements the optimal enhancement-layer prediction, enabled by transform domain resampling that ensures the base layer information is maximally accessible and usable at the enhancement layer despite their differing signal resolutions, with an optimal approach to quantization and entropy coding that exploits all available information, encapsulated in the appropriate conditional distribution for transform coefficients, to yield a unified coding engine for spatial scalability. For such quantizers to fully exploit base layer information, the enhancement layer transform block size must proportionally match the signal block transformed at the base layer. The overall system incorporates switching that applies the full estimation-theoretic quantizer and entropy coder at the right block size, but may optionally employ other block sizes where it defaults to optimal prediction followed by standard quantization. It is experimentally shown that the proposed scheme provides considerable performance gains over conventional codec and other leading competitors.
Joint Optimization of Base and Enhancement Layers in Scalable Audio Coding

Current scalable audio coders typically optimize performance at a particular layer without regard to impact on other layers, and are thus unable to provide a performance trade-off between different layers. In the particular case of MPEG Scalable Advanced Audio Coding (S-AAC) and Scalable-to-Lossless (SLS) coding, the base-layer is optimized first followed by successive optimization of higher layers, which ensures optimality of the base-layer but results in a scalability penalty that progressively increases with the enhancement layer index. The ability to trade-off performance between different layers enables alignment to the real world requirement for audio quality commensurate with the bandwidth afforded by a user. This work provides the means to better control the performance tradeoffs, and the distribution of the scalability penalty, between the base and enhancement layers. Specifically, it proposes an efficient joint optimization algorithm that selects the encoding parameters for each layer while accounting for the rate-distortion costs in all layers. The efficacy of the technique is demonstrated in the two distinct settings of S-AAC, and SLS High Definition Advanced Audio Coding. Objective and subjective tests provide evidence for substantial gains, and represent a significant step toward bridging the gap with the non-scalable coder.
Additive successive refinement

Scalable coding rate-distortion bounds, and conditions for their coincidence with non-scalable rate-distortion bounds, were derived by W.H.R. Equitz and T.M. Cover (1991) and B. Rimoldi (1994). However, there has always been an implicit assumption of a tree structured vector quantization (TSVQ) scheme, which requires extensive memory usage. On the other hand, in most practical applications, additive structures such as multi-stage vector quantization (MSVQ) are preferred. In this paper, we analyze the bounds for MSVQ.
Unequally protected multistage vector quantization for time-varying CDMA channels

We present a source-channel coding system for operation over code-division multiple-access channels with time-varying conditions. The proposed system consists of a multistage vector quantizer (MSVQ) in conjunction with unequal protection against channel errors. The receiver estimates the channel conditions and decodes as many stages of the quantizer as can be reliably decoded. The approach to system design and optimization is first derived and evaluated for a system that employs hard decoding of stage indices. The approach is then extended to the more general case of weighted decoding. Simulation results are given for transmission of Gauss-Markov sources over broadcast and slow fading channels. Consistent and substantial improvement is achieved over the standard MSVQ with equal error protection, and the gains, in terms of source signal-to-noise ratio, are in the range of 3-5 dB.
Modeling and evaluation for electrical characteristics of through-strata-vias (TSVS) in three-dimensional integration

This paper discusses through-strata-vias (TSVs) technology and presents modeling results of their electrical performance using Agilent's ADS and Momentum simulator. Since TSV is an essential component in three-dimensional (3D) integration/packaging, it is important to explore and investigate its electrical characteristics. A simple face-to-back TSV is studied in frequency domain and time domain. The impact of physical configurations and materials on TSV electrical characteristics is evaluated. An equivalent circuit model is proposed, and the values of passive elements (resistance, inductance and capacitance) within the model are extracted from full-wave scattering parameters.
Gaussian sensor networks with adversarial nodes

This paper studies a particular sensor network model which involves one single Gaussian source observed by many sensors, subject to additive independent Gaussian observation noise. Sensors communicate with the receiver over an additive Gaussian multiple access channel. The aim of the receiver is to reconstruct the underlying source with minimum mean squared error. The scenario of interest here is one where some of the sensors act as adversary (jammer): they strive to maximize distortion. We show that the ability of transmitter sensors to secretly agree on a random event, that is “coordination”, plays a key role in the analysis. Depending on the coordination capability of sensors and the receiver, we consider two problem settings. The first setting involves transmitters with “coordination” capabilities in the sense that all transmitters can use identical realization of randomized encoding for each transmission. In this case, the optimal strategy for the adversary sensors also requires coordination, where they all generate the same realization of independent and identically distributed Gaussian noise. In the second setting, the transmitter sensors are restricted to use fixed, deterministic encoders and this setting, which corresponds to a Stackelberg game, does not admit a saddle-point solution. We show that the the optimal strategy for all sensors is uncoded communications where encoding functions of adversaries and transmitters are in opposite directions. For both settings, digital compression and communication is strictly suboptimal.
Silicon assembler design of a DCT/IDCT ASIC for real-time JPEG/MPEG compression

A single multiplier skewed pipeline architecture is presented for real time computation of the 1D discrete cosine transform (DCT)/IDCT in a 2- mu m process. This architecture is extended to the 2-D case using a single 1-D DCT module by an appropriate choice of algorithm. Silicon compilation techniques are used, and the design of three module generators is examined.<<ETX>>
Estimation-theoretic approach to delayed prediction in scalable video coding

Scalable video coding (SVC) employs inter-frame prediction at the base and/or the enhancement layers. Since the base layer can be encoded/decoded independent of the enhancement layers, we consider here the potential gains when prediction at the enhancement layers is delayed to accumulate and incorporate additional future information from the base layer. We build on two basic estimationtheoretic (ET) approaches developed by our group: an ET approach for enhancement layer prediction that optimally combines current base layer with prior enhancement layer information, and our recent ET approach for delayed decoding. The proposed technique fully exploits all the available information from the base layer, including any future frame information, and past enhancement layer information. It achieves considerable gains over zero-delay techniques including both standard SVC, and SVC with optimal ET prediction (but with zero encoding delay).
Constrained-storage vector quantization with a universal codebook

Many image compression techniques require the quantization of multiple vector sources with significantly different distributions. With vector quantization (VQ), these sources are optimally quantized using separate codebooks, which may collectively require an enormous memory space. Since storage is limited in most applications, a convenient way to gracefully trade between performance and storage is needed. Earlier work addressed this problem by clustering the multiple sources into a small number of source groups, where each group shares a codebook. We propose a new solution based on a size-limited universal codebook that can be viewed as the union of overlapping source codebooks. This framework allows each source codebook to consist of any desired subset of the universal code vectors and provides greater design flexibility which improves the storage-constrained performance. A key feature of this approach is that no two sources need be encoded at the same rate. An additional advantage of the proposed method is its close relation to universal, adaptive, finite-state and classified quantization. Necessary conditions for optimality of the universal codebook and the extracted source codebooks are derived. An iterative design algorithm is introduced to obtain a solution satisfying these conditions. Possible applications of the proposed technique are enumerated, and its effectiveness is illustrated for coding of images using finite-state vector quantization, multistage vector quantization, and tree-structured vector quantization.
Combinatorial message sharing and random binning for multiple description coding

This paper proposes a new multiple description (MD) coding method and an associated achievable rate-distortion region for L ≥ 2 channels. The proposed scheme randomly bins codebooks chosen from the codebook structure, similar to that of the recently proposed combinatorial message sharing (CMS) scheme designed for conditional codebook encoding. The proposed scheme effectively performs multilayer random binning for each subset of the description, which hence enables to utilize the symmetry of a “subset” of the description rates wherever it exists. The new scheme specializes in to the conventional multilayer random binning as an extreme special case.
On scalable coding of hidden Markov sources

While several real world signals, such as speech, image and sensor network data, are modeled as hidden Markov sources (HMS) for recognition and analysis applications, their typical compression exploits temporal correlations by modeling them as simple (non-hidden) Markov sources. However, the inherent hidden Markov nature of these sources implies that an observed sample depends, in fact, on all past observations, thus rendering simple Markov modeling suboptimal. Motivated by this realization, previous work from our lab derived a technique to optimally quantize and compress HMS. In this paper we build on, and considerably extend, these results to the problem of scalable coding of HMS. At the base layer, as proposed earlier, the approach tracks an estimate of the state probability distribution and adapts the encoder structure accordingly. At the enhancement layer, the state probability distribution is refined using available information from past enhancement layer reconstructed samples, and this refined estimate is further combined with quantization information from the base layer, to effectively characterize the probability density of the current sample conditioned on all available information. We update code parameters on the fly, at each observation, at both the encoder and the decoder and at both layers. Experimental results validate the superiority of the proposed approach with considerable gains over standard predictive coding employing a simple Markov model.
High-level DSP synthesis using the COMET design system

The authors address methodologies for high-level synthesis of dedicated digital signal processing (DSP) architectures using the cluster-oriented and minimum execution time (COMET) design system. The system is tuned to the synthesis of DSP ASICs from behavioral specifications written in VHDL. COMET is capable of generating more efficient architectures using innovative scheduling and resource allocation algorithm which exploit the cluster information and maximize the parallel tasks. With these transformations, major improvements are achieved with fewer registers and interconnections; an industrial quality design is then derived in both FIR and elliptic filter examples.<<ETX>>
Time-division versus superposition coded modulation schemes for unequal error protection

This paper is concerned with the design problem of coded modulation schemes which provide unequal error protection (UEP) against Gaussian noise. Starting with the case of two bit streams, we consider two classes of UEP schemes: time-division coded modulation (TDCM) and superposition coded modulation (SCM). The early result of Bergmanns and Cover (1974) ensures the asymptotic superiority of SCM. However, accounting for the fact that practical channel codes do not achieve capacity, we show that the validity of this result depends on the prescribed degree of inequality in protection of the streams. To complement the straightforward design and use of TDCM schemes, we propose methods to decode and design SCM schemes. The design examples substantiate the conclusions obtained from performance analysis. We extend the results to the case of UEP of multiple (more than two) bit streams, where a hybrid of SCM and TDCM must also be considered. We show that, for a large range of unequal protection levels, the hybrid scheme outperforms both SCM and TDCM.
Constrained Clustering as an Optimization Method

A deterministic annealing approach to clustering is derived on the basis of the principle of maximum entropy. This approach is independent of the initial state and produces natural hierarchical clustering solutions by going through a sequence of phase transitions. It is modified for a larger class of optimization problems by adding constraints to the free energy. The concept of constrained clustering is explained, and three examples are are given in which it is used to introduce deterministic annealing. The previous clustering method is improved by adding cluster mass variables and a total mass constraint. The traveling salesman problem is reformulated as constrained clustering, yielding the elastic net (EN) approach to the problem. More insight is gained by identifying a second Lagrange multiplier that is related to the tour length and can also be used to control the annealing process. The open path constraint formulation is shown to relate to dimensionality reduction by self-organization in unsupervised learning. A similar annealing procedure is applicable in this case as well.
Deterministic annealing for trellis quantizer and HMM design using Baum-Welch re-estimation

The deterministic annealing algorithm for data clustering is extended to address the trellis quantizer design problem. The approach is derived within information theory and probability theory, using the principle of maximum entropy to induce a distribution over all possible path encodings of the training set. The resulting method is intimately connected to estimation procedures on Markov chains. Performance gains over known methods are obtained for memoryless, multimodal scalar sources as well as for the vector Gaussian and Laplacian sources. The method is also suggested for an estimation problem in hidden Markov models. For a Gaussian mixture state example, this approach achieves a greater likelihood value than the best result of standard Baum-Welch re-estimation, based on numerous initializations within the data.<<ETX>>
Rotational motion model for temporal prediction in 360 video coding

The recent boom in the field of virtual and augmented reality has dramatically increased the prevalence of spherical video. Given the enormous amount of data consumed by spherical video, it is critical to achieve efficient compression for storage and transmission. Prevalent approaches simply project (via different geometries) the spherical video onto planes for processing with traditional 2D video coding standards. However, such approaches are significantly sub-optimal as standard video coders only allow for block translations in the critical tool of motion compensated prediction, which is incompatible with the expected motion in projected spherical video. Specifically, the effective sampling density varies over the sphere and the resulting locally varying warping yields complex non-linear motion in the projected domain. Hence, translation in the projected domain does not preserve an object's shape and size on the sphere, and its corresponding motion vector does not have a useful physical interpretation. Instead, we propose to characterize the motion directly on the sphere with a rotational motion model, specifically, in terms of sphere rotations along geodesics. This model preserves object shape and size on the sphere. A motion vector in this model implicitly specifies an axis of rotation and the degree of rotation about that axis, to convey the actual motion of objects on the sphere. Complementary to the novel motion model, we further propose an effective motion search technique that is tailored to the sphere's geometry. Experimental results demonstrate that the proposed framework achieves significant gains over prevalent motion models, across various projection geometries.
A modified distortion metric for audio coding

Current audio coding standards employ the modified discrete cosine transform (MDCT) where overlapped frames of audio are windowed and transformed to the frequency domain. Encoding parameters are chosen so as to minimize a distortion measure subject to a rate constraint. At the decoder, inverse transformation involves additional windowing and overlap-add of frames. An analysis of the time domain error in the reconstructed frame reveals that distortion metrics based solely on the MDCT domain error are in fact unable to capture the effects of windowing and overlap-add at the decoder. The main contribution of this paper is a modified distortion metric that does capture these effects via modified discrete sine transform analysis. When incorporated into an Advanced Audio Coder the proposed distortion metric significantly improves subjective quality of reconstructed audio.
On Optimal Coding of Hidden Markov Sources

The hidden Markov model (HMM) is widely used to model processes in several real world applications, including speech processing and recognition, image understanding and sensor networks. A problem of concern is that of quantization of the sequence of observations generated by an HMM, which is referred as a hidden Markov source (HMS). Despite the importance of the problem, and the well-defined structure of the process, there has been very limited work addressing the optimal quantization of HMS, and conventional approaches focus on optimization of parameters of known quantization schemes. This paper proposes a method that directly tracks the state probability distribution of the underlying source and optimizes the encoder structure according to the estimated HMS status. Unlike existing approaches, no stationarity assumption is needed, and code parameters are updated on they: with each observation, both the encoder and the decoder refine the estimated probability distribution over the states. The main approach is then specialized to a practical variant involving switched quantizers, and an algorithm that iteratively optimizes the quantize codebooks is derived. Numerical results show superiority of the proposed approach over prior methods.
Evaluation of motion estimation procedure for low-frame-rate aerial images

In an attempt to improve the quality of displacement estimations between frames in low-frame-rate aerial sequences, a displacement estimation algorithm related to frame-differencing techniques combined with an affine model of motion was tested. The algorithm follows the development of Lenz (1984) in which points on displaced gray-level edges are used to estimate the local apparent horizontal and vertical displacements with the subsequent global motion parameters for translation, rotation, and zoom calculated through linear regression. Performance measurements were made with real video sequences. Effects of scene content and flight parameters were observed along with effects of frame transmission rate, image coding procedures, and channel errors for simulated transmission. The algorithm worked well, producing correct matches about 90% of the time, and was largely unaffected by image coding procedures or channel errors.<<ETX>>
On multidimensional optimal estimators: Linearity conditions

It is well-known that, when a multivariate Gaussian source is contaminated with Gaussian noise, a linear estimator minimizes the mean square estimation error, irrespective of the covariance matrices of both source and noise. This paper analyzes the conditions for linearity of optimal estimators for general source and noise distributions over vector spaces. Given a noise (or source) distribution, we derive conditions for existence and uniqueness of a matching source (or noise) distribution that renders the optimal estimator linear. Moreover, we establish a new characterization of the uniqueness of Gaussians: the multivariate Gaussian source-channel pair is the only pair for which the optimal estimator is linear at more than one signal-to-noise ratio.