nils-herrmann/clean_scirepeval huggingface dataset:
Electric field dependence of TDDB activation energy in ultrathin oxides

A study of the electric field dependence of the TDDB activation energy is presented for 12 nm down to 4.7 nm thin oxides. It is shown that the TDDB activation energy depends linearly on the stress electric field and that this behavior depends strongly on the oxide thickness. Moreover, a relationship between the TDDB activation energy attenuation per WV/cm and the oxide thickness has been found. As will be demonstrated, these results are of great importance for the rigorous estimation of the oxide lifetime of both present and future technologies.
Fusion of multiple uncertainty estimators and propagators for noise robust ASR

Uncertainty decoding has been successfully used for speech recognition in highly nonstationary noise environments. Yet, accurate estimation of the uncertainty on the denoised signals and propagation to the features remain difficult. In this work, we propose to fuse the uncertainty estimates obtained from different uncertainty estimators and propagators by linear combination. The fusion coefficients are optimized by minimizing a measure of divergence with oracle estimates on development data. Using the Kullback-Leibler divergence, we obtain 18% relative error rate reduction on the 2nd CHiME Challenge with respect to conventional decoding, that is about twice as much as the reduction achieved by the best single uncertainty estimator and propagator.
Hot-carrier to cold-carrier device lifetime modeling with temperature for low power 40nm Si-bulk NMOS and PMOS FETs

The persistence of hot-carrier degradation down to low voltages is analyzed in recent CMOS nodes through the effect of multivibration excitation (MVE) of the Si-H bonds and deexcitation by multiphonon emission. This new mechanism is described by an energy framework and originates from the channel current density independently of voltage and geometry. MVE mode is enhanced with temperature in NMOS and PMOS devices due to its strong coupling to the lattice disregarding the negative bias contribution in PMOS.
Fast DNN training based on auxiliary function technique

Deep neural networks (DNN) are typically optimized with stochastic gradient descent (SGD) using a fixed learning rate or an adaptive learning rate approach (ADAGRAD). In this paper, we introduce a new learning rule for neural networks that is based on an auxiliary function technique without parameter tuning. Instead of minimizing the objective function, a quadratic auxiliary function is recursively introduced layer by layer which has a closed-form optimum. We prove the monotonic decrease of the new learning rule. Our experiments show that the proposed algorithm converges faster and to a better local minimum than SGD. In addition, we propose a combination of the proposed learning rule and ADAGRAD which further accelerates convergence. Experimental evaluation on the MNIST database shows the benefit of the proposed approach in terms of digit recognition accuracy.
Multi-source TDOA estimation using SNR-based angular spectra

This paper deals with the localization of multiple sources from two-channel mixtures recorded in a reverberant environment. We introduce new angular spectrum-based methods relying on the signal-to-noise ratio (SNR) to estimate the time difference of arrival (TDOA) of each source. We propose and compare five ways of estimating the SNR in each time-frequency point and in each direction, using beamforming techniques and statistical models. Large-scale evaluation considering a high number of situations shows the effectiveness of the proposed approach compared to state-of-the-art angular spectrum-based techniques.
Fast bayesian nmf algorithms enforcing harmonicity and temporal continuity in polyphonic music transcription

This article presents theoretical and experimental results about constrained non-negative matrix factorization (NMF) in a Bayesian framework, enforcing both spectral harmonicity and temporal continuity. We exhibit fast multiplicative update rules to perform the decomposition, which are then applied to perform polyphonic piano music transcription. This approach is shown to outperform other standard NMF-based transcription systems, providing a meaningful mid-level representation of the data.
DNN Uncertainty Propagation Using GMM-Derived Uncertainty Features for Noise Robust ASR

The uncertainty decoding framework is known to improve the deep neural network (DNN)-based automatic speech recognition (ASR) performance in noisy environments. It operates by estimating the statistical uncertainty about the input features and propagating it to the output senone posteriors by sampling. Unfortunately, this approximate propagation scheme limits the performance improvement. In this letter, we exploit the fact that uncertainty propagation can be achieved in closed form for Gaussian mixture acoustic models (GMMs). We introduce new GMM-derived (GMMD) uncertainty features for the robust DNN-based acoustic model training and decoding. The GMMD features are computed as the difference between the GMM log-likelihoods obtained with versus without uncertainty. They are concatenated with conventional acoustic features and used as inputs to the DNN. We evaluate the resulting ASR performance on the CHiME-2 and CHiME-3 datasets. The proposed features are shown to improve the performance on both datasets, both for the conventional decoding and for the uncertainty decoding with different uncertainty estimation/propagation techniques.
Is audio signal processing still useful in the era of machine learning?

Audio signal processing has long been the obvious approach to problems such as microphone array processing, active noise control, or speech enhancement. Yet, it is increasingly being challenged by black-box machine learning approaches based on, e.g., deep neural networks (DNN), which have already achieved superior results on certain tasks. In this talk, I will try to convince that machine learning approaches shouldn't be disregarded, but that black boxes won't solve these problems either. There is hence an opportunity for signal processing researchers to join forces with machine learning researchers and solve these problems together. I will provide examples of this multi-disciplinary approach for audio source separation and robust automatic speech recognition.
Hot-Carrier acceleration factors for low power management in DC-AC stressed 40nm NMOS node at high temperature

Channel Hot-Carrier degradation presents a renewed interest in the last NMOS nodes where the device reliability of bulk silicon (core) 40nm and Input/Output (IO) device is difficult to achieve at high temperature as a function of supply voltage VDD and back bias V<inf>BS</inf>. A three mode interface trap generation is proposed based on the energy acquisition involved in distinct interactions in all the V<inf>GS</inf>, V<inf>DS</inf> (V<inf>BS</inf>) conditions as a single I<inf>DS</inf> lifetime dependence is observed with V<inf>GD</inf> > 0. This gives a new age(t) function useful for accurate DC to AC transfers. Positive temperature activation is explained by the rise of ionization rate with electron-electron scattering (medium I<inf>DS</inf>) and multi vibrational excitation (higher I<inf>DS</inf>) which increase the H desorption by thermal emission. The use of forward VBS has shown no gain under CHC for both device types. The main limitation occurs under reverse V<inf>BS</inf> = −V<inf>DD</inf> in IO where the smaller temperature activation partially compensates the larger damage. In that case a security margin can be established giving a limit of V<inf>BS</inf> = −V<inf>DD</inf>/2 for design reliability.
An experimental comparison of source separation and beamforming techniques for microphone array signal enhancement

We consider the problem of separating one or more speech signals from a noisy background. Although blind source separation (BSS) and beamforming techniques have both been exploited in this context, the former have typically been applied to small microphone arrays and the latter to larger arrays. In this paper, we provide an experimental comparison of some established beamforming and post-filtering techniques on the one hand and modern BSS techniques involving advanced spectral models on the other hand. We analyze the results as a function of the number of microphones, the number of speakers and the input Signal-to-Noise Ratio (iSNR) w.r.t. multichannel real-world environmental noise recordings. The results of the comparison show that, provided that a suitable post-filter or spectral model is chosen, beamforming performs similar to BSS on average in the single-speaker case while in the two-speaker case BSS exceeds beamformer performance. Crucially, this claim holds independently of the number of microphones.
Under-Determined Reverberant Audio Source Separation Using a Full-Rank Spatial Covariance Model

This paper addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial characteristics of the source. We then consider four specific covariance models, including a full-rank unconstrained model. We derive a family of iterative expectation-maximization (EM) algorithms to estimate the parameters of each model and propose suitable procedures adapted from the state-of-the-art to initialize the parameters and to align the order of the estimated sources across all frequency bins. Experimental results over reverberant synthetic mixtures and live recordings of speech data show the effectiveness of the proposed approach.
Evidence for defect-generation-driven wear-out of breakdown conduction path in ultra thin oxides

This paper considers the physical mechanisms responsible for the progressive (i.e. smooth or noisy) breakdown manifestation commonly measured on ultra-thin oxides (Tox<25 /spl Aring/). First, it is verified that the theory previously published is relevant by highlighting progressive behavior predicted on thicker oxides (50 /spl Aring/). Second, the stored energy is shown not to be correlated to the progressive behavior even if it influences the failure and its occurrence. At last, the progressiveness being gate voltage and temperature driven, it is stated that the defect generation probability drives the breakdown degradation after its creation. This is proven by measuring the influence on the progressiveness of a bulk bias applied during the stress of a pMOS in the inversion regime.
A general variational Bayesian framework for robust feature extraction in multisource recordings

We consider the problem of extracting features from individual sources in a multisource audio recording using a general source separation algorithm. The main issue is to estimate and propagate the uncertainty over the separated source signals, so as to robustly estimate the features despite source separation errors. While state-of-the-art techniques estimate the uncertainty in a heuristic manner, we propose to integrate over the parameter space of the source separation algorithm. We apply variational Bayes to estimate the posterior probability of the sources and subsequently derive the expectation of the features by moment matching. Experiments over stereo mixtures of three or four sources show that the proposed method provides the best results in terms of the root mean square (RMS) error on the estimated features.
Subjective and Objective Quality Assessment of Audio Source Separation

We aim to assess the perceived quality of estimated source signals in the context of audio source separation. These signals may involve one or more kinds of distortions, including distortion of the target source, interference from the other sources or musical noise artifacts. We propose a subjective test protocol to assess the perceived quality with respect to each kind of distortion and collect the scores of 20 subjects over 80 sounds. We then propose a family of objective measures aiming to predict these subjective scores based on the decomposition of the estimation error into several distortion components and on the use of the PEMO-Q perceptual salience measure to provide multiple features that are then combined. These measures increase correlation with subjective scores up to 0.5 compared to nonlinear mapping of individual state-of-the-art source separation measures. Finally, we released the data and code presented in this paper in a freely available toolkit called PEASS.
Review on high-k dielectrics reliability issues

High-k gate dielectrics, particularly Hf-based materials, are likely to be implemented in CMOS advanced technologies. One of the important challenges in integrating these materials is to achieve lifetimes equal or better than their SiO/sub 2/ counterparts. In this paper we review the status of reliability studies of high-k gate dielectrics and try to illustrate it with experimental results. High-k materials show novel reliability phenomena related to the asymmetric gate band structure and the presence of fast and reversible charge. Reliability of high-k structures is influenced both by the interfacial layer as well as the high-k layer. One of the main issues is to understand these new mechanisms in order to asses the lifetime accurately and reduce them.
Enforcing Harmonicity and Smoothness in Bayesian Non-Negative Matrix Factorization Applied to Polyphonic Music Transcription

This paper presents theoretical and experimental results about constrained non-negative matrix factorization (NMF) in a Bayesian framework. A model of superimposed Gaussian components including harmonicity is proposed, while temporal continuity is enforced through an inverse-Gamma Markov chain prior. We then exhibit a space-alternating generalized expectation-maximization (SAGE) algorithm to estimate the parameters. Computational time is reduced by initializing the system with an original variant of multiplicative harmonic NMF, which is described as well. The algorithm is then applied to perform polyphonic piano music transcription. It is compared to other state-of-the-art algorithms, especially NMF-based. Convergence issues are also discussed on a theoretical and experimental point of view. Bayesian NMF with harmonicity and temporal continuity constraints is shown to outperform other standard NMF-based transcription systems, providing a meaningful mid-level representation of the data. However, temporal smoothness has its drawbacks, as far as transients are concerned in particular, and can be detrimental to transcription performance when it is the only constraint used. Possible improvements of the temporal prior are discussed.
Investigating stranded GMM for improving automatic speech recognition

This paper investigates recently proposed Stranded Gaussian Mixture acoustic Model (SGMM) for Automatic Speech Recognition (ASR). This model extends conventional hidden Markov model (HMM-GMM) by explicitly introducing dependencies between components of the observation Gaussian mixture densities. The main objective of the paper is to experimentally study, how useful SGMM can be for dealing with data, which contains different sources of acoustic variability. First studied sources of variability are age and gender in quiet environment (TIdigits task including child speech). Second, the SGMM modeling is applied on data produced by different speakers and corrupted by non-stationary noise (CHiME 2013 challenge data). Finally, SGMM is applied on the same noisy data, but after performing speech enhancement (i.e., the remaining variability mostly comes from residual noise and different speakers). Although SGMM was originally proposed for robust speech recognition of noisy data, in this work it was found, that the model is more efficient for handling speaker variability in quiet environment.
A prototype system for object coding of musical audio

This article deals with low bitrate object coding of musical audio, and more precisely with the extraction of pitched sound objects in polyphonic music. After a brief review of existing methods, we discuss the potential benefits of recasting this problem in a Bayesian framework. We define pitched objects by a set of probabilistic priors and derive efficient algorithms to infer active objects and their parameters. Preliminary experiments suggest that the proposed method results in a better sound quality than simple sinusoidal coding while achieving a lower bitrate.
Improved method for the oxide thickness extraction in MOS structures with ultra-thin gate dielectrics

An improved method for the assessment of the oxide thickness applicable to advanced CMOS technologies is proposed. To this end, a proper combination of Maserjian's technique (Maserjian et al., Solid State Electron. vol. 17, pp. 335-9, 1974) and of Vincent's method (Vincent et al.,  Microelectronic Test Structures vol. 10, pp. 105-10, 1997) is used to alleviate the unknown parameter inherent to both extraction procedures and which depends on the employed carrier statistics. The new method has been successfully applied to various technologies with gate oxide thickness ranging from 7 nm down to 1.8 nm.
Discriminative importance weighting of augmented training data for acoustic model training

DNN based acoustic models require a large amount of training data. Parametric data augmentation techniques such as adding noise, reverberation, or changing the speech rate, are often employed to boost the dataset size and the ASR performance. The choice of augmentation techniques and the associated parameters has been handled heuristically so far. In this work we propose an algorithm to automatically weight data perturbed using a variety of augmentation techniques and/or parameters. The weights are learned in a discriminative fashion so as to minimize the frame error rate using the standard gradient descent algorithm in an iterative manner. Experiments were performed using the CHiME-3 dataset. Data augmentation was done by adding noise at different SNRs. A relative WER improvement of 15% was obtained with the proposed data weighting algorithm compared to the unweighted augmented dataset. Interestingly, the resulting distribution of SNRs in the weighted training set differs significantly from that of the test set.
Off state incorporation into the 3 energy mode device lifetime modeling for advanced 40nm CMOS node

Hot-Carrier degradation is analyzed with 3 mode lifetime modeling extended to the cases of PMOSFETs and Off state modes in last CMOS nodes. Damage worsens in subthreshold region with positive temperature activation due to interface traps generation in the gate-drain overlap (GDO) and localized charge trapping into the spacer oxide. Care has been done on the distinct impact of the measuring bias and stressing conditions in Sub-VT regime. The latter can be much more degraded than On-state parameters showing the amphoteric nature of Si-H bonds breaking rates throughout the channel-GDO. Off-mode damage has been included in the 3 mode energy device lifetime giving a useful modeling for any AC waveforms suitable for digital to analog operations.
Stability Analysis of Multiplicative Update Algorithms and Application to Nonnegative Matrix Factorization

Multiplicative update algorithms have proved to be a great success in solving optimization problems with nonnegativity constraints, such as the famous nonnegative matrix factorization (NMF) and its many variants. However, despite several years of research on the topic, the understanding of their convergence properties is still to be improved. In this paper, we show that Lyapunov's stability theory provides a very enlightening viewpoint on the problem. We prove the exponential or asymptotic stability of the solutions to general optimization problems with nonnegative constraints, including the particular case of supervised NMF, and finally study the more difficult case of unsupervised NMF. The theoretical results presented in this paper are confirmed by numerical simulations involving both supervised and unsupervised NMF, and the convergence speed of NMF multiplicative updates is investigated.
Blind RT60 estimation robust across room sizes and source distances

The reverberation time or RT60 is an essential acoustic parameter of a room. In many situations, the room impulse response (RIR) is not available and the RT60 must be blindly estimated from a speech or music signal. Current methods often implicitly assume that reverberation dominates direct sound, which restricts their applicability to relatively small rooms or distant sound sources. This paper features two contributions. Firstly, we propose a blind RT60 estimation method that is independent of the room size and the source distance by preprocessing the input signal using a beamformer to cancel direct sound and early echoes. Secondly, we perform the largest experimental evaluation to our knowledge using a set of 342 RIRs. We show that the estimation error is significantly reduced even in the case when reverberation dominates.
Blind Criterion and Oracle Bound for Instantaneous Audio Source Separation using Adaptive Time-Frequency Representations

The separation of multichannel audio mixtures is often addressed by the masking approach, which consists of representing the mixture signal in the time-frequency domain and associating each time-frequency bin with a small number of active sources. Adaptive time-frequency representations can increase the disjointness of the sources compared to fixed representations. However their use has not been conclusive so far. In this paper, we propose a new criterion for the blind estimation of an adapted representation of an instantaneous mixture and explain how to compute the oracle representation leading to the best possible performance given reference source signals. Experimental results suggest that a small separation performance improvement can indeed be achieved using adaptive representations, but that complementary approaches must be investigated to obtain larger improvements.
New Insights Into Recovery Characteristics During PMOS NBTI and CHC Degradation

In this paper, we investigate the recovery characteristics associated with negative bias temperature instability and channel hot carrier degradation in p-channel metal-oxide-semiconductor using a novel method of on-the-fly patterns of biases. We demonstrate experimentally that we can identify two types of mechanisms associated with degradation. One of them is associated with recovery dynamics, and the other relates to nonrecoverable degradation.
New method to extract interface states density at the back and the front gate interfaces of FDSOI transistors from CV-GV measurements

A novel method is proposed to extract interface state density Dit at both Front and Back Gate interfaces. This accurate technique based on CV and GV measurements, enables to measure low Dit densities ~10<sup>10</sup>traps/cm<sup>2</sup>/eV, at both interfaces on standard FDSOI transistors. In particular, it was found that DitBG is very low ~3.10<sup>10</sup> traps/cm<sup>2</sup>/eV, and about one decade smaller than Dit<inf>FG</inf>=5.10<sup>11</sup> traps/cm<sup>2</sup>/eV.
Audio source localization by optimal control of a mobile robot

We consider the task of audio source localization using a microphone array on a mobile robot. Active localization algorithms have been proposed in the literature that can estimate the 3D position of a source by fusing the measurements taken for different poses of the robot. The robot movements are typically fixed, however, or they obey heuristic strategies, such as turning the head and moving towards the source, which may be suboptimal. In this paper, we propose to control the robot movements so as to locate the source as quickly as possible. We represent the belief about the source position by a discrete grid and we introduce a dynamic programming algorithm to find the optimal robot motion minimizing the entropy of the grid. We report initial results in a real environment.