nils-herrmann/clean_scirepeval huggingface dataset:
Low Bit-Rate Object Coding of Musical Audio Using Bayesian Harmonic Models

This paper deals with the decomposition of music signals into pitched sound objects made of harmonic sinusoidal partials for very low bit-rate coding purposes. After a brief review of existing methods, we recast this problem in the Bayesian framework. We propose a family of probabilistic signal models combining learned object priors and various perceptually motivated distortion measures. We design efficient algorithms to infer object parameters and build a coder based on the interpolation of frequency and amplitude parameters. Listening tests suggest that the loudness-based distortion measure outperforms other distortion measures and that our coder results in a better sound quality than baseline transform and parametric coders at 8 and 2 kbit/s. This work constitutes a new step towards a fully object-based coding system, which would represent audio signals as collections of meaningful note-like sound objects
Polyphonic Pitch Estimation and Instrument Identification by Joint Modeling of Sustained and Attack Sounds

Polyphonic pitch estimation and musical instrument identification are some of the most challenging tasks in the field of music information retrieval (MIR). While existing approaches have focused on the modeling of harmonic partials, we design a joint Gaussian mixture model of the harmonic partials and the inharmonic attack of each note. This model encodes the power of each partial over time as well as the spectral envelope of the attack part. We derive an expectation-maximization (EM) algorithm to estimate the pitch and the parameters of the notes. We then extract timbre features both from the harmonic and the attack part via principal component analysis (PCA) over the estimated model parameters. Musical instrument recognition for each estimated note is finally carried out with a support vector machine (SVM) classifier. Experiments conducted on mixtures of isolated notes as well as real-world polyphonic music show higher accuracy over state-of-the-art approaches based on the modeling of harmonic partials only.
Multichannel Audio Source Separation With Deep Neural Networks

This article addresses the problem of multichannel audio source separation. We propose a framework where deep neural networks (DNNs) are used to model the source spectra and combined with the classical multichannel Gaussian model to exploit the spatial information. The parameters are estimated in an iterative expectation-maximization (EM) fashion and used to derive a multichannel Wiener filter. We present an extensive experimental study to show the impact of different design choices on the performance of the proposed technique. We consider different cost functions for the training of DNNs, namely the probabilistically motivated Itakura-Saito divergence, and also Kullback-Leibler, Cauchy, mean squared error, and phase-sensitive cost functions. We also study the number of EM iterations and the use of multiple DNNs, where each DNN aims to improve the spectra estimated by the preceding EM iteration. Finally, we present its application to a speech enhancement problem. The experimental results show the benefit of the proposed multichannel approach over a single-channel DNN-based approach and the conventional multichannel nonnegative matrix factorization-based iterative EM algorithm.
The second ‘chime’ speech separation and recognition challenge: Datasets, tasks and baselines

Distant-microphone automatic speech recognition (ASR) remains a challenging goal in everyday environments involving multiple background sources and reverberation.  Two separate tracks have been proposed: a small-vocabulary task with small speaker movements and a medium-vocabulary task without speaker movements. We discuss the rationale for the challenge and provide a detailed description of the datasets, tasks and baseline performance results for each track.
Extension of uncertainty propagation to dynamic MFCCS for noise robust ASR

Uncertainty propagation has been successfully employed for speech recognition in nonstationary noise environments. The uncertainty about the features is typically represented as a diagonal covariance matrix for static features only. We present a framework for estimating the uncertainty over both static and dynamic features as a full covariance matrix. The estimated covariance matrix is then multiplied by scaling coefficients optimized on development data. We achieve 21% relative error rate reduction on the 2nd CHiME Challenge with respect to conventional decoding without uncertainty, that is five times more than the reduction achieved with diagonal uncertainty covariance for static features only.
Quasi-breakdown in ultra-thin oxides: some insights on the physical mechanisms

This paper discusses the variation of the probability to observe quasi-breakdown with capacitor topology and stress conditions and gets some insights on the physical mechanisms of both breakdown and quasi-breakdown phenomena. To achieve this goal, a methodology based on competing mechanisms between breakdown and quasi-breakdown is introduced in order to rigorously analyze Time Dependent Dielectric Quasi-breakdown. This approach is found to provide specific and distinct parameters for breakdown and quasi-breakdown, such as Weibull slope, acceleration factor and activation energy, which enable to well model the quasi-breakdown rate behavior with the capacitor topology and the stress conditions. This good adequation first validate this methodology and undoubtedly indicates that the physical defects at the origin of both phenomena are different. Additionally, the quasi-breakdown is demonstrated to occur on a reduced window of carrier energy, what confirms the distinct origins of both phenomena. Moreover, this induces some important consequences concerning reliability extrapolation, since the critical failure mode can be different at high field than at nominal condition.
Estimating the Structural Segmentation of Popular Music Pieces Under Regularity Constraints

Music structure estimation has recently emerged as a central topic within the field of music information retrieval. Indeed, as music is a highly structured information stream, knowledge of how a music piece is organized represents a key challenge to enhance the management and exploitation of large music collections. This paper focuses on the benefits that can be expected from a regularity constraint on the structural segmentation of popular music pieces. Specifically, here, we study how a constraint that favors structural segments of comparable size provides a better conditioning of the boundary estimation process. First, we propose a formulation of the structural segmentation task as an optimization process, which separates the contribution from the audio features and the one from the constraint. We illustrate how the corresponding cost function can be minimized using a Viterbi algorithm. We present briefly its implementation and results in three systems designed for and submitted to the MIREX 2010, 2011, and 2012 evaluation campaigns. Then, we explore the benefits of the regularity constraint as an efficient mean for combining the outputs of a selection of systems presented at MIREX between 2010 and 2015, yielding a level of performance competitive to that of the state-of-the-art on the “MIREX10” dataset (100 J-Pop songs from the RWC database).
Hot-carrier damage in AC-stressed deep submicrometer CMOS technologies

We have investigated the degradation behavior of single transistors using DC and AC alternating stress conditions as the interface trap generation and the charge trapping/detrapping phenomena become a competitive interaction during AC cycles in 0.25 /spl mu/m CMOS technologies with 5 nm thick gate-oxide. In this way, we determine to what extent the effects and mechanisms are affecting the resultant degradation behavior between N- and P-MOSFETs in order to explain the degradation observed in actual circuits. The effect of temperature is further investigated between -40/spl deg/C and 125/spl deg/C as Negative Bias Temperature Instability (NBTI), thermal emission, field-enhanced charge detrapping may contribute to the transistor degradation and modify the circuit degradation. We verify the usefulness of the experimental procedure and model based on duty cycle calculations for ring oscillators.
The second ‘CHiME’ speech separation and recognition challenge: An overview of challenge systems and outcomes

Distant-microphone automatic speech recognition (ASR) remains a challenging goal in everyday environments involving multiple background sources and reverberation. This paper reports on the results of the 2nd `CHiME' Challenge, an initiative designed to analyse and evaluate the performance of ASR systems in a real-world domestic environment. We discuss the rationale for the challenge and provide a summary of the datasets, tasks and baseline systems. The paper overviews the systems that were entered for the two challenge tracks: small-vocabulary with moving talker and medium-vocabulary with stationary talker. We present a summary of the challenge findings including novel results produced by challenge system combination. Possible directions for future challenges are discussed.
Probabilistic Factor Oracles for Multidimensional Machine Improvisation

This article presents two methods to generate automatic improvisation using training over multidimensional sequences. We consider musical features such as melody, harmony, timbre, etc., as dimensions. We first present a system combining interpolated probabilistic models with a factor oracle. The probabilistic models are trained on a corpus of musical work to learn the correlation between dimensions, and they are used to guide the navigation in the factor oracle to ensure a logical improvisation. Improvisations are therefore created in a way in which the intuition of a context is enriched with multidimensional knowledge. We then introduce a system creating multidimensional improvisations based on communication between dimensions via probabilistic message passing. The communication infers some anticipatory behavior on each dimension influenced by the others, creating a consistent multidimensional improvisation. Both systems were evaluated by professional improvisers during listening sessions. Overall, the systems received good feedback and showed encouraging results—first, on how multidimensional knowledge can improve navigation in the factor oracle and, second, on how communication through message passing can emulate the interactivity between dimensions or musicians.
Designing the Wiener post-filter for diffuse noise suppression using imaginary parts of inter-channel cross-spectra

This paper describes a new design of the Wiener post-filter for diffuse noise suppression. The Wiener post-filter is well-known as an effective post-processing of the minimum variance distortionless response beamformer, and its output is the optimal estimate of the target signal in the sense of the minimum mean square error. It is essential to accurately estimate the target power spectrum from the observed signals contaminated by noise when designing the Wiener post-filter. In our method, it is estimated from the imaginary parts of the inter-channel observation cross-spectra, under the assumption that the inter-channel noise cross-spectra are real-valued. The post-filter is designed using the estimate and this design is shown to be effective even for a small-sized array through experiments using simulated and real environmental noise.
Benchmarking flexible adaptive time-frequency transforms for underdetermined audio source separation

We have implemented several fast and flexible adaptive lapped orthogonal transform (LOT) schemes for underdetermined audio source separation. This is generally addressed by time-frequency masking, requiring the sources to be disjoint in the time-frequency domain. We have already shown that disjointness can be increased via adaptive dyadic LOTs. By taking inspiration from the windowing schemes used in many audio coding frameworks, we improve on earlier results in two ways. Firstly, we consider non-dyadic LOTs which match the time-varying signal structures better. Secondly, we allow for a greater range of overlapping window profiles to decrease window boundary artifacts. This new scheme is benchmarked through oracle evaluations, and is shown to decrease computation time by over an order of magnitude compared to using very general schemes, whilst maintaining high separation performance and flexible signal adaptivity. As the results demonstrate, this work may find practical applications in high fidelity audio source separation.
A study of speech distortion conditions in real scenarios for speech processing applications

The growing demand for robust speech processing applications able to operate in adverse scenarios calls for new evaluation protocols and datasets beyond artificial laboratory conditions. The characteristics of real data for a given scenario are rarely discussed in the literature. As a result, methods are often tested based on the author expertise and not always in scenarios with actual practical value. This paper aims to open this discussion by identifying some of the main problems with data simulation or collection procedures used so far and summarizing the important characteristics of real scenarios to be taken into account, including the properties of reverberation, noise and Lombard effect. At last, we provide some preliminary guidelines towards designing experimental setup and speech recognition results for proposal validation.
Dynamic Bayesian Networks for Symbolic Polyphonic Pitch Modeling

Symbolic pitch modeling is a way of incorporating knowledge about relations between pitches into the process of analyzing musical information or signals. In this paper, we propose a family of probabilistic symbolic polyphonic pitch models, which account for both the “horizontal” and the “vertical” pitch structure. These models are formulated as linear or log-linear interpolations of up to five sub-models, each of which is responsible for modeling a different type of relation. The ability of the models to predict symbolic pitch data is evaluated in terms of their cross-entropy, and of a newly proposed “contextual cross-entropy” measure. Their performance is then measured on synthesized polyphonic audio signals in terms of the accuracy of multiple pitch estimation in combination with a Nonnegative Matrix Factorization-based acoustic model. In both experiments, the log-linear combination of at least one “vertical” (e.g., harmony) and one “horizontal” (e.g., note duration) sub-model outperformed a pitch-dependent Bernoulli prior by more than 60% in relative cross-entropy and 3% in absolute multiple pitch estimation accuracy. This work provides a proof of concept of the usefulness of model interpolation, which may be used for improved symbolic modeling of other aspects of music in the future.
Variational Bayesian Inference for Source Separation and Robust Feature Extraction

We consider the task of separating and classifying individual sound sources mixed together. The main challenge is to achieve robust classification despite residual distortion of the separated source signals. A promising paradigm is to estimate the uncertainty about the separated source signals and to propagate it through the subsequent feature extraction and classification stages. We argue that variational Bayesian (VB) inference offers a mathematically rigorous way of deriving uncertainty estimators, which contrasts with state-of-the-art estimators based on heuristics or on maximum likelihood (ML) estimation. We propose a general VB source separation algorithm, which makes it possible to jointly exploit spatial and spectral models of the sources. This algorithm achieves 6% and 5% relative error reduction compared to ML uncertainty estimation on the CHiME noise-robust speaker identification and speech recognition benchmarks, respectively, and it opens the way for more complex VB approximations of uncertainty.
Robust ASR using neural network based speech enhancement and feature simulation

We consider the problem of robust automatic speech recognition (ASR) in the context of the CHiME-3 Challenge. The proposed system combines three contributions. First, we propose a deep neural network (DNN) based multichannel speech enhancement technique, where the speech and noise spectra are estimated using a DNN based regressor and the spatial parameters are derived in an expectation-maximization (EM) like fashion. Second, a conditional restricted Boltzmann machine (CRBM) model is trained using the obtained enhanced speech and used to generate simulated training and development datasets. The goal is to increase the similarity between simulated and real data, so as to increase the benefit of multicondition training. Finally, we make some changes to the ASR backend. Our system ranked 4th among 25 entries.
A thorough investigation of progressive breakdown in ultra-thin oxides. Physical understanding and application for industrial reliability assessment

This paper provides a close investigation of the gate oxide failure for thickness below 24/spl Aring/. At first, the failure detection is discussed showing that its manifestation is not catastrophic any more. Then, the wear-out beginning at the failure occurrence is studied. It highlights the path conduction aging whose progressiveness is found to be mainly gate voltage driven. At last, progressiveness dynamics is investigated and a methodology is developed to rigorously relax the reliability criterion following applications.
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

Speech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial preprocessing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this paper, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: 1) the acoustic impulse response model, 2) the spatial filter design criterion, 3) the parameter estimation algorithm, and 4) optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.
Model for NBTI in p-MOSFETs with ultra thin nitrided gated oxides

Negative bias temperature instability (NBTI) in p-MOSFETs is known as a major reliability issue for microelectronic devices. It has been suggested that NBTI is related to the generation of defects at the Si/SiO/sub 2/ interface during the electrical stress. In this work, the degradation of the threshold voltage, mobility and the drain current of p-MOSFET's with ultrathin nitrided gate oxides during NBTI stress is modelled providing a physical basis as to why nitridation of oxides increases NBTI. This model includes a gaussian spread of activation energies of the Si/sub 3//spl equiv/Si/spl middot/ centers (so called P/sub b0/ centres), as well as the field dependence of the mean activation energy.
Fusion Methods for Speech Enhancement and Audio Source Separation

A wide variety of audio source separation techniques exist and can already tackle many challenging industrial issues. However, in contrast with other application domains, fusion principles were rarely investigated in audio source separation despite their demonstrated potential in classification tasks. In this paper, we propose a general fusion framework which takes advantage of the diversity of existing separation techniques in order to improve separation quality. We obtain new source estimates by summing the individual estimates given by different separation techniques weighted by a set of fusion coefficients. We investigate three alternative fusion methods which are based on standard nonlinear optimization, Bayesian model averaging, or deep neural networks. Experiments conducted for both speech enhancement and singing voice extraction demonstrate that all the proposed methods outperform traditional model selection. The use of deep neural networks for the estimation of time-varying coefficients notably leads to large quality improvements, up to 3 dB in terms of signal-to-distortion ratio compared to model selection.
Effects of audio coding on ICA performance: An experimental study

In this paper, we study the influence of lossy audio coding on the performance of Independent Component Analysis (ICA). In particular, we derive two compression scenarios from practical implementations. In the first case, we consider the situation when the sources are independently compressed, decompressed and then mixed. In the second, we consider the situation when mixtures of sources are jointly compressed. We experimentally show that the modification of the spectro-temporal diversity due to compression has almost no effect on the performance of ICA methods. We also show that the two tested stereo encoding strategies have a major effect on the performance of ICA, especially when the mixed signals are compressed at low bit rates. As these strategies have been extended to audio systems involving much more than two channels, our work suggests that ICA will not be able to successfully process the high number of observations provided by modern immersive audio systems.
General algorithms for estimating spectrogram and transfer functions of target signal for blind suppression of diffuse noise

We propose two algorithms for jointly estimating the power spectrogram and the room transfer functions of a target signal in diffuse noise. These estimates can be used to design a multichannel Wiener filter, and thereby separate a target signal from an unknown direction from diffuse noise. We express a diffuse noise model as a subspace of a matrix linear space, which consists of Hermitian matrices instead of Euclidean vectors. This general framework enables the design of new general algorithms applicable to all specific noise models, instead of multiple specific algorithms each applicable to a single model. The more general proposed algorithms resulted in superior noise suppression performance to our previous algorithms in terms of an output signal-to-noise ratio (SNR).
Performance measurement in blind audio source separation

In this paper, we discuss the evaluation of blind audio source separation (BASS) algorithms. Depending on the exact application, different distortions can be allowed between an estimated source and the wanted true source. We consider four different sets of such allowed distortions, from time-invariant gains to time-varying filters. In each case, we decompose the estimated source into a true source part plus error terms corresponding to interferences, additive noise, and algorithmic artifacts. Then, we derive a global performance measure using an energy ratio, plus a separate performance measure for each error term. These measures are computed and discussed on the results of several BASS problems with various difficulty levels