nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Channel Audio Source Separation Using Multiple Deformed References

We present a general multi-channel source separation framework where additional audio references are available for one (or more) source(s) of a given mixture. Each audio reference is another mixture which is supposed to contain at least one source similar to one of the target sources. Deformations between the sources of interest and their references are modeled in a linear manner using a generic formulation. This is done by adding transformation matrices to an excitation-filter model, hence affecting different axes, namely frequency, dictionary component or time. A nonnegative matrix co-factorization algorithm and a generalized expectation-maximization algorithm are used to estimate the parameters of the model. Different model parameterizations and different combinations of algorithms are tested on music plus voice mixtures guided by music and/or voice references and on professionally-produced music recordings guided by cover references. Our algorithms improve the signal-to-distortion ratio (SDR) of the sources with the lowest intensity by 9 to 15 decibels (dB) with respect to original mixtures.
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 power dissipated is shown not to be correlated to the progressive behavior even if it influences the failure and its occurrence. Finally, the progression being gate voltage and temperature driven, it is shown that the defect generation probability drives the breakdown degradation after its creation. This is proven by measuring the influence on the progression of a bulk bias applied during the stress of a pMOS device in the inversion regime.
A compressed sensing approach to the simultaneous recording of multiple room impulse responses

We consider the estimation of multiple room impulse responses from the simultaneous recording of several known sources. Existing techniques are restricted to the case where the number of sources is at most equal to the number of sensors. We relax this assumption in the case where the sources are known. To this aim, we propose statistical models of the filters associated with convex log-likelihoods, and we propose a convex optimization algorithm to solve the inverse problem with the resulting penalties. We provide a comparison between penalties via a set of experiments which shows that our method allows to speed up the recording process with a controlled quality tradeoff.
Nonparametric Uncertainty Estimation and Propagation for Noise Robust ASR

We consider the framework of uncertainty propagation for automatic speech recognition (ASR) in highly nonstationary noise environments. Uncertainty is considered as the variance of speech distortion. Yet, its accurate estimation in the spectral domain and its propagation to the feature domain remain difficult. Existing methods typically rely on a single uncertainty estimator and propagator fixed by mathematical approximation. In this paper, we propose a new paradigm where we seek to learn more powerful mappings to predict uncertainty from data. We investigate two such possible mappings: linear fusion of multiple uncertainty estimators/propagators and nonparametric uncertainty estimation/propagation. In addition, a procedure to propagate the estimated spectral-domain uncertainty to the static Mel frequency cepstral coefficients (MFCCs), to the log-energy, and to their first- and second-order time derivatives is proposed. This results in a full uncertainty covariance matrix over both static and dynamic MFCCs. Experimental evaluation on Tracks 1 and 2 of the 2nd CHiME Challenge resulted in up to 29% and 28% relative keyword error rate reduction with respect to speech enhancement alone.
Performance & reliability of 3D architectures (πfet, Finfet, Ωfet)

The impact of 3D architectures and boosters on the trade-off performance/reliability is deeply investigated in this paper. “Finfet” transistor presents a slight superior trade-off than square shaped “πfet” device because of little improved performance and similar BTI&HC reliability. “Ωfet” also offers a better compromise than πfet due to improved BTI reliability. Finally strained SOI & SiGeOI devices are highly suitable since they allow boosting the transistor performance without any reliability penalty.
Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation

Multiple pitch estimation consists of estimating the fundamental frequencies and saliences of pitched sounds over short time frames of an audio signal. This task forms the basis of several applications in the particular context of musical audio. One approach is to decompose the short-term magnitude spectrum of the signal into a sum of basis spectra representing individual pitches scaled by time-varying amplitudes, using algorithms such as nonnegative matrix factorization (NMF). Prior training of the basis spectra is often infeasible due to the wide range of possible musical instruments. Appropriate spectra must then be adaptively estimated from the data, which may result in limited performance due to overfitting issues. In this paper, we model each basis spectrum as a weighted sum of narrowband spectra representing a few adjacent harmonic partials, thus enforcing harmonicity and spectral smoothness while adapting the spectral envelope to each instrument. We derive a NMF-like algorithm to estimate the model parameters and evaluate it on a database of piano recordings, considering several choices for the narrowband spectra. The proposed algorithm performs similarly to supervised NMF using pre-trained piano spectra but improves pitch estimation performance by 6% to 10% compared to alternative unsupervised NMF algorithms.
Multichannel music separation with deep neural networks

This article addresses the problem of multichannel music separation. We propose a framework where the source spectra are estimated using deep neural networks and combined with spatial covariance matrices to encode the source spatial characteristics. The parameters are estimated in an iterative expectation-maximization fashion and used to derive a multichannel Wiener filter. We evaluate the proposed framework for the task of music separation on a large dataset. Experimental results show that the method we describe performs consistently well in separating singing voice and other instruments from realistic musical mixtures.
Variational Bayesian model averaging for audio source separation

Non-negative Matrix Factorization (NMF) has become popular in audio source separation in order to design source-specific models. The number of components of the NMF is known to have a noticeable influence on separation quality. Many methods have thus been proposed to select the best order for a given task. To go further, we propose here to use model averaging. As existing techniques do not allow an effective averaging, we introduce a generative model in which the number of components is a random variable and we propose a modification to conventional variational Bayesian (VB) inference. Experimental results on synthetic data show promising results as our model leads to better separation results and is less computationally demanding than conventional VB model selection.
High-k dielectrics breakdown accurate lifetime assessment methodology

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. The reliability of high-k structures is influenced both by the interfacial layer as well as the high-k layer. In this paper, we propose an accurate statistical analysis of the breakdown linking the progressive breakdown to the breakdown of the interfacial layer and the soft breakdown or hard breakdown to the high-k layer breakdown. The breakdown of the two layers are found to be independent. Based on this analysis, we study the degradation mechanisms and we demonstrate that the multi vibrational hydrogen release (MVHR) process is the prevalent mechanism which drives the creation of a percolation path through the interfacial layer.
Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation

In this work we present a new Bayesian topic model: latent hierarchical Pitman-Yor process allocation (LHPYA), which uses hierarchical Pitman-Yor process priors for both word and topic distributions, and generalizes a few of the existing topic models, including the latent Dirichlet allocation (LDA), the bigram topic model and the hierarchical Pitman-Yor topic model. Using such priors allows for integration of n-grams with a topic model, while smoothing them with the state-of-the-art method. Our model is evaluated by measuring its perplexity on a dataset of musical genre and harmony annotations 3 Genre Database (3GDB) and by measuring its ability to predict musical genre from chord sequences. In terms of perplexity, for a 262-chord dictionary we achieve a value of 2.74, compared to 18.05 for trigrams and 7.73 for a unigram topic model. In terms of genre prediction accuracy with 9 genres, the proposed approach performs about 33% better in relative terms than genre-dependent n-grams, achieving 60.4% of accuracy.
A General Flexible Framework for the Handling of Prior Information in Audio Source Separation

Most audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the mixing process. In this paper, we introduce a general audio source separation framework based on a library of structured source models that enable the incorporation of prior knowledge about each source via user-specifiable constraints. While this framework generalizes several existing audio source separation methods, it also allows to imagine and implement new efficient methods that were not yet reported in the literature. We first introduce the framework by describing the model structure and constraints, explaining its generality, and summarizing its algorithmic implementation using a generalized expectation-maximization algorithm. Finally, we illustrate the above-mentioned capabilities of the framework by applying it in several new and existing configurations to different source separation problems. We have released a software tool named Flexible Audio Source Separation Toolbox (FASST) implementing a baseline version of the framework in Matlab.
Impact of backside interface on Hot Carriers degradation of thin film FDSOI Nmosfets

Based on simulation results, we show that defects at the Si/Box interface of FDSOI transistors can have a detrimental impact on reliability. In particular, attention is paid to Hot Carriers degradations (HC) on ultra thin film FDSOI NMOSFETs for which defects can be created very close to the back gate interface. A new technique based on capacitance measurements is proposed to localize HC degradation at front gate and/or back gate interface on FDSOI transistors. Thanks to this method, it is shown that, similarly to bulk technologies, only the front gate interface is degraded during a classical HC stress. Finally, despite the presence of an additional Si/BOx interface, FDSOI NMOSFETs down to 30nm gate length exhibit HC lifetimes over 10 years, even when a back bias is applied.
Introducing a simple fusion framework for audio source separation

We propose in this paper a simple fusion framework for un-derdetermined audio source separation. This framework can be applied to a wide variety of source separation algorithms providing that they estimate time-frequency masks. Fusion principles have been successfully implemented for classification tasks. Although it is similar to classification, audio source separation does not usually take advantage of such principles. We thus introduce some general fusion rules inspired by classification and we evaluate them in the context of voice extraction. Experimental results are promising as our proposed fusion rule can improve separation results up to 1 dB in SDR.
Quasi-breakdown in ultra-thin SiO/sub 2/ films: occurrence characterization and reliability assessment methodology

This paper discusses different statistical approaches for the quasi-breakdown phenomenon. In particular, a novel methodology based on the idea that breakdown and quasi-breakdown are competing mechanisms and that they have to be separately analyzed, is developed and well validated for oxide thickness ranging from 3.5 down to 2.5 nm. This methodology is demonstrated to well explain all the quasi-breakdown rate variations with temperature, voltage, area and oxide thickness. Moreover, this new approach enables to rigorously determine the quasi-breakdown acceleration factor with temperature and electric field, which have been found to be different from the breakdown ones. As a result, and confirmed by the difference observed between the obtained time to breakdown and time to quasi-breakdown spreads, the defects at the origin of both phenomena have to be different. Finally, a reliability assessment methodology is presented enabling a proper analysis of both phenomena for reliability evaluation and lifetime prediction.
Under-determined convolutive blind source separation using spatial covariance models

This paper deals with the problem 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 properties of the source. We consider two covariance models and address the estimation of their parameters from the recorded mixture by a suitable initialization scheme followed by an iterative expectation-maximization (EM) procedure in each frequency bin. We then align the order of the estimated sources across all frequency bins based on their estimated directions of arrival (DOA). Experimental results over a stereo reverberant speech mixture show the effectiveness of the proposed approach.
The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines

The CHiME challenge series aims to advance far field speech recognition technology by promoting research at the interface of signal processing and automatic speech recognition. This paper presents the design and outcomes of the 3rd CHiME Challenge, which targets the performance of automatic speech recognition in a real-world, commercially-motivated scenario: a person talking to a tablet device that has been fitted with a six-channel microphone array. The paper describes the data collection, the task definition and the baseline systems for data simulation, enhancement and recognition. The paper then presents an overview of the 26 systems that were submitted to the challenge focusing on the strategies that proved to be most successful relative to the MVDR array processing and DNN acoustic modeling reference system. Challenge findings related to the role of simulated data in system training and evaluation are discussed.
Unified Perspective of NBTI and Hot-Carrier Degradation in CMOS using on-the-Fly Bias Patterns

This work views NBTI and various conditions of channel hot carrier (CHC) degradation in PMOS and NMOS devices from a unified perspective. This is accomplished by a novel technique using sequential application of stress biases and monitoring the degradation on-the-fly. Thereby, we are able to observe and segregate the distinct mechanisms co-existing during a particular condition of degradation. In particular, we gain critical insights into recovery phenomena, which are observed during certain conditions of CHC degradation (Mistry et al., 1991) as well as during NBTI (Rangan, 2003). These findings set the stage for consistent physical models for degradation as well as for design simulation under multiple operating modes
Stability of capacitance voltage linearity for high-k MIM capacitor

The need for increased MIM capacitance density has led to the introduction of new high-k dielectric materials into both CMOS and BiCMOS technologies. These materials, Ta/sub 2/O/sub 5/ and Al/sub 2/O/sub 3/, exhibit several non-idealities which require more careful study than more conventional SiO/sub 2/ and SiN dielectrics. In particular, the evolution and physical mechanisms for C versus V non linearity as a function of both frequency and temperature are reported, as well as the high temperature capacitance hysteresis and capacitance linearity variations over time. The characterization and understanding of these non-idealities is required to guarantee their acceptable performance for analog IC MIM applications.
Modeling the Dynamic Variability Induced by Charged Traps in a Bilayer Gate Oxide

In this paper, we revisit the classic single layer defect centric model (DCM), largely used in reliability studies, in the more realistic case of bilayer gate oxide transistors integrating an interface layer and a high-K dielectric. The Monte Carlo method and 3-D electrostatic simulations are used to determine the impact of the traps present in both layers on the Vt of transistors. It is proved that the DCM is able to capture the trap-induced variability of bilayer transistors but with effective model parameters, which have no more a true physical meaning as in the case of the single layer gate oxide. An extended DCM, accounting for a two trap distributions, is then proposed to better explain the degradation measured on bilayer transistors. Finally, this extended DCM finds another application in the evaluation of the bias temperature instability-induced variability of static RAM cells.
An investigation of discrete-state discriminant approaches to single-sensor source separation

This paper investigated a new scheme for single-sensor audio source separation. This framework is introduced comparatively to the existing Gaussian mixture model generative approach and is focusing on the mixture states rather than on the source states, resulting in a discrete, joint state discriminant approach. The study establishes the theoretical performance bounds of the proposed scheme and an actual source separation system is designed. The performance is computed on a set of musical recordings and a discussion is proposed, including the question of the source correlation and the possible drawbacks of the method.
Limiting oxide failure mode versus oxide thickness. Some insights for deep-submicron technologies

This paper focuses on the different aspects related to the gate oxide reliability for oxide thicknesses ranging between 7 and 2.5 nm in order to get some insights on the failure modes which will dominantly limit the future technologies' dielectric reliability. First, the SILC bell-shaped behavior with the oxide thickness presages that the SILC will not be a dielectric reliability limitation for the future CMOS technologies. Second, the impact of the larger spread of the intrinsic breakdown distribution on the oxide lifetime is underlined. Third, the quasi-breakdown occurrence is characterized and a methodology is proposed in order to statistically analyze quasi-breakdown phenomenon. Finally, the question of the oxide limiting failure mode is developed: for oxide thicknesses ranging between 5 and 3 nm, quasi-breakdown phenomenon appears to be the most limiting failure mode; on the contrary, for 2.5 nm oxide and probably thinner, the breakdown event occurs before the quasi-breakdown one at nominal conditions.
Recursive Bayesian estimation of the acoustic noise emitted by wind farms

Wind turbine noise is often annoying for humans living in close proximity to a wind farm. Reliably estimating the intensity of wind turbine noise is a necessary step towards quantifying and reducing annoyance, but it is challenging because of the overlap with background noise sources. Current approaches involve measurements with on/off turbine cycles and acoustic simulations, which are expensive and unreliable. This raises the problem of separating the noise of wind turbines from that of background noise sources and coping with the uncertainties associated with the source separation output. In this paper we propose to assist a black-box source separation system with a model of wind turbine noise emission and propagation in a recursive Bayesian estimation framework. We validate our approach on real data with simulated uncertainties using different nonlinear Kalman filters.
Nonnegative matrix factorization and spatial covariance model for under-determined reverberant audio source separation

We address the problem of blind audio source separation in the under-determined and convolutive case. The contribution of each source to the mixture channels in the time-frequency domain is modeled by a zero-mean Gaussian random vector with a full rank covariance matrix composed of two terms: a variance which represents the spectral properties of the source and which is modeled by a nonnegative matrix factorization (NMF) model and another full rank covariance matrix which encodes the spatial properties of the source contribution in the mixture. We address the estimation of these parameters by maximizing the likelihood of the mixture using an expectation-maximization (EM) algorithm. Theoretical propositions are corroborated by experimental studies on stereo reverberant music mixtures.
Harmonic and inharmonic Nonnegative Matrix Factorization for Polyphonic Pitch transcription

Polyphonic pitch transcription consists of estimating the onset time, duration and pitch of each note in a music signal. This task is difficult in general, due to the wide range of possible instruments. This issue has been studied using adaptive models such as Nonnegative Matrix Factorization (NMF), which describe the signal as a weighted sum of basis spectra. However basis spectra representing multiple pitches result in inaccurate transcription. To avoid this, we propose a family of constrained NMF models, where each basis spectrum is expressed as a weighted sum of narrowband spectra consisting of a few adjacent partials at harmonic or inharmonic frequencies. The model parameters are adapted via combined multiplicative and Newton updates. The proposed method is shown to outperform standard NMF on a database of piano excerpts.
Music separation guided by cover tracks: Designing the joint NMF model

In audio source separation, reference guided approaches are a class of methods that use reference signals to guide the separation. In prior work, we proposed a general framework to model the deformation between the sources and the references. In this paper, we investigate a specific scenario within this framework: music separation guided by the multitrack recording of a cover interpretation of the song to be processed. We report a series of experiments highlighting the relevance of joint Non-negative Matrix Factorization (NMF), dictionary transformation, and specific transformation models for different types of sources. A signal-to-distortion ratio improvement (SDRI) of almost 11 decibels (dB) is achieved, improving by 2 dB compared to previous study on the same data set. These observations contribute to validate the relevance of the theoretical general framework and can be useful in practice for designing models for other reference guided source separation problems.