The mantle transition zone beneath South America from stacking of P-to-S receiver functions

The Mantle Transition Zone (MTZ) beneath South America is investigated with stacking of Receiver Functions (RFs) of converted P-to-S phases from the velocity discontinuities at 410 km (d410) and 660 km (d660). A total of 785 seismic stations provided 22,235 high quality RFs, allowing 1,717 MTZ thickness measurements in circular bins of 2⁰ radius. An apparent MTZ structure is derived using the IASP91 reference model, where the d410 mean depth is 409.1 km and the d660 is 661.5 km, and the mean MTZ thickness is 251.8 km. This model presents coherent depressions and uplifts of the discontinuities matching well known velocity anomalies. After experimenting with eleven tomography models, a velocity correction adopting the SAW642ANb model with a 50 km top layer of the JOINT model was found to be the best approach towards a true depth model. This model wields a mean d410 at 413.2 km and the d660 at 662.8 km, whereas the MTZ thickness is 249.6 km. The correlation of depths and MTZ thickness variations supports previously determined Clapeyron Slopes (γ) of 2 MPa/K for the d410 and -3 MPa/K for the d660, reconciling γ with seismic observations. The results are fateful to the tectonic structure of South America, where colder-than-normal MTZ anomalies are found along the Andes, while hotter-than-normal anomalies are found along the Atlantic coast. The latter observed MTZ characteristics spatially correspond well with rift related structures and the former to locations where subducted slab has been inferred. The main inference from these observations is that tectonic processes play a major role in the control of thermal and chemical proprieties of the MTZ --Abstract, page iii

Smoluchowski dynamics and the ergodic-nonergodic transition

We use the recently introduced theory for the kinetics of systems of classical particles to investigate systems driven by Smoluchowski dynamics. We investigate the existence of ergodic-nonergodic (ENE) transitions near the liquid-glass transition. We develop a self-consistent perturbation theory in terms of an effective two-body potential. We work to second order in this potential. At second order we have an explicit relationship between the static structure factor and the effective potential. We choose the static structure factor in the case of hard spheres to be given by the solution of the Percus-Yevick approximation for hard spheres. Then using the analytically determined ENE equation for the ergodicity function we find an ENE transition for packing fraction, eta, greater than a critical value eta*=0.76 which is physically unaccessible. The existence of a linear fluctuation-dissipation theorem in the problem is shown and used to great advantage.Comment: 51 pages, 6 figure

Do current-density nonlinearities cut off the glass transition?

Extended mode coupling theories for dense fluids predict that nonlinear current-density couplings cut off the singular `ideal glass transition', present in the standard mode coupling theory where such couplings are ignored. We suggest here that, rather than allowing for activated processes as sometimes supposed, contributions from current-density couplings are always negligible close to a glass transition. We discuss in schematic terms how activated processes can nonetheless cut off the transition, by causing the memory function to become linear in correlators at late times.Comment: 4 page

Instrumental and perceptual evaluation of dereverberation techniques based on robust acoustic multichannel equalization

Speech signals recorded in an enclosed space by microphones at a distance from the speaker are often corrupted by reverberation, which arises from the superposition of many delayed and attenuated copies of the source signal. Because reverberation degrades the signal, removing reverberation would enhance quality. Dereverberation techniques based on acoustic multichannel equalization are known to be sensitive to room impulse response perturbations. In order to increase robustness, several methods have been proposed, as for example, using a shorter reshaping filter length, incorporating regularization, or applying a sparsity-promoting penalty function. This paper focuses on evaluating the performance of these methods for single-source multi-microphone scenarios, using instrumental performance measures as well as using subjective listening tests. By analyzing the correlation between the instrumental and the perceptual results, it is shown that signal-based performance measures are more advantageous than channel-based performance measures to evaluate the perceptual speech quality of signals that were dereverberated by equalization techniques. Furthermore, this analysis also demonstrates the need to develop more reliable instrumental performance measures

Exploring auditory-inspired acoustic features for room acoustic parameter estimation from monaural speech

Room acoustic parameters that characterize acoustic environments can help to improve signal enhancement algorithms such as for dereverberation, or automatic speech recognition by adapting models to the current parameter set. The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters. In this paper, we propose a blind ROom Parameter Estimator (ROPE) based on an artificial neural network that learns the mapping to discrete ranges of the RT and the ELR from single-microphone speech signals. Auditory-inspired acoustic features are used as neural network input, which are generated by a temporal modulation filter bank applied to the speech time-frequency representation. ROPE performance is analyzed in various reverberant environments in both clean and noisy conditions for both fullband and subband RT and ELR estimations. The importance of specific temporal modulation frequencies is analyzed by evaluating the contribution of individual filters to the ROPE performance. Experimental results show that ROPE is robust against different variations caused by room impulse responses (measured versus simulated), mismatched noise levels, and speech variability reflected through different corpora. Compared to state-of-the-art algorithms that were tested in the acoustic characterisation of environments (ACE) challenge, the ROPE model is the only one that is among the best for all individual tasks (RT and ELR estimation from fullband and subband signals). Improved fullband estimations are even obtained by ROPE when integrating speech-related frequency subbands. Furthermore, the model requires the least computational resources with a real time factor that is at least two times faster than competing algorithms. Results are achieved with an average observation window of 3 s, which is important for real-time applications

Joint estimation of reverberation time and early-to-late reverberation ratio from single-channel speech signals

The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters commonly used to characterize acoustic room environments. In contrast to conventional blind estimation methods that process the two parameters separately, we propose a model for joint estimation to predict the RT and the ELR simultaneously from single-channel speech signals from either full-band or sub-band frequency data, which is referred to as joint room parameter estimator (jROPE). An artificial neural network is employed to learn the mapping from acoustic observations to the RT and the ELR classes. Auditory-inspired acoustic features obtained by temporal modulation filtering of the speech time-frequency representations are used as input for the neural network. Based on an in-depth analysis of the dependency between the RT and the ELR, a two-dimensional (RT, ELR) distribution with constrained boundaries is derived, which is then exploited to evaluate four different configurations for jROPE. Experimental results show that-in comparison to the single-task ROPE system which individually estimates the RT or the ELR-jROPE provides improved results for both tasks in various reverberant and (diffuse) noisy environments. Among the four proposed joint types, the one incorporating multi-task learning with shared input and hidden layers yields the best estimation accuracies on average. When encountering extreme reverberant conditions with RTs and ELRs lying beyond the derived (RT, ELR) distribution, the type considering RT and ELR as a joint parameter performs robustly, in particular. From state-of-the-art algorithms that were tested in the acoustic characterization of environments challenge, jROPE achieves comparable results among the best for all individual tasks (RT and ELR estimation from full-band and sub-band signals)

Measuring, modelling and predicting perceived reverberation

This paper investigates the relationship between the perceived level of reverberation and parameters measured from the room impulse response (RIR), as well as the design of an instrumental measure that predicts this perceived level. We first present the results of an experimental listening test conducted to assess the level of perceived reverberation in speech captured by a single microphone, before analysing the gathered data to assess the influence of parameters such as the reverberation time (T60) or the direct-to-reverberant ratio (DRR). Secondly, we use the results of this analysis to improve the signal based reverberation decay tail (RDT) measure, previously proposed by the authors to predict the perceived level of reverberation. The accuracy of the proposed measure is evaluated in terms of correlation with the subjective scores and compared to the performance of predictors using parameters extracted from the RIR. Results show that the proposed modifications to the RDT does improve its accuracy. Though still slightly outperformed by measures based on parameters of the RIR, we believe the proposed measure to be useful in scenarios in which the RIR or its parameters are unknown

Non-intrusive speech quality prediction using modulation energies and LSTM-network

Many signal processing algorithms have been proposed to improve the quality of speech recorded in the presence of noise and reverberation. Perceptual measures, i.e., listening tests, are usually considered the most reliable way to evaluate the quality of speech processed by such algorithms but are costly and time-consuming. Consequently, speech enhancement algorithms are often evaluated using signal-based measures, which can be either intrusive or non-intrusive. As the computation of intrusive measures requires a reference signal, only non-intrusive measures can be used in applications for which the clean speech signal is not available. However, many existing non-intrusive measures correlate poorly with the perceived speech quality, particularly when applied over a wide range of algorithms or acoustic conditions. In this paper, we propose a novel non-intrusive measure of the quality of processed speech that combines modulation energy features and a recurrent neural network using long short-term memory cells. We collected a dataset of perceptually evaluated signals representing several acoustic conditions and algorithms and used this dataset to train and evaluate the proposed measure. Results show that the proposed measure yields higher correlation with perceptual speech quality than that of benchmark intrusive and non-intrusive measures when considering various categories of algorithms. Although the proposed measure is sensitive to mismatch between training and testing, results show that it is a useful approach to evaluate specific algorithms over a wide range of acoustic conditions and may, thus, become particularly useful for real-time selection of speech enhancement algorithm settings