Small-world behavior in a system of mobile elements

We analyze the propagation of activity in a system of mobile automata. A number r L^d of elements move as random walkers on a lattice of dimension d, while with a small probability p they can jump to any empty site in the system. We show that this system behaves as a Dynamic Small-World (DSW) and present analytic and numerical results for several quantities. Our analysis shows that the persistence time T* (equivalent to the persistence size L* of small-world networks) scales as T* ~ (r p)^(-t), with t = 1/(d+1).Comment: To appear in Europhysics Letter

Symmetry-preserving discretization of the incompressible form of the Navier-Stokes equations under turbulent conditions. LES simulation of a turbulent channel flow

The incompressible form of the Navier-Stokes equations (conservation of mass, momentum and energy) is solved by applying a second-order symmetry-preserving spatial discretization which allows to preserve the symmetry of the operators. The physics behind turbulent flows and how those can be modelled is studied, considering both the RANS equations and the LES model. The Taylor-Green vortex problem is solved with no model and compared with the results of van Rees et al. [4], obtaining very good agreement regarding the time evolution of the volume-averaged kinetic energy, but higher discrepancies in the time evolution of the kinetic energy dissipation rate. Additionally, DNS results for a turbulent channel flow at Reτ “ 180 are obtained with coarse meshes. The same problem is also solved by applying the Smagorinsky, S3PR and Vreman’s LES models. DNS results obtained with a 323 mesh show relatively good agreement with the reference results of Moser et al. [5], while LES simulations employing the S3PR and Vreman’s model allow to improve the results in the buffer-layer region

Numerical study of the Navier-Stokes equations using the Fractional Step Method. Application to the laminar flow around a square cylinder.

The numerical resolution of the incompressible Navier-Stokes equations with the Fractional Step Method, based on the Helmholtz-Hodge theorem, is studied. Basic benchmark problems are solved previously, such as a generic transient 2D heat conduction problem, potential flow around a rotating and non-rotating cylinder and a generic convection-diffusion equation; with excellent agreement with the results obtained and the ones on the literature. The code for the incompressible Navier-Stokes equation is verified using the benchmark results of the Lid-driven cavity problem with really good agreement as well. Finally, laminar flow around a confined square cylinder is studied and compared with the results from Breuer et. al. The drag coefficient and Strouhal number are computed finding good agreement for Reynolds numbers lower than 100 but important discrepancies for higher Reynolds

Jitter and Shimmer measurements for speaker diarization

Jitter and shimmer voice quality features have been successfully used to characterize speaker voice traits and detect voice pathologies. Jitter and shimmer measure variations in the fundamental frequency and amplitude of speaker's voice, respectively. Due to their nature, they can be used to assess differences between speakers. In this paper, we investigate the usefulness of these voice quality features in the task of speaker diarization. The combination of voice quality features with the conventional spectral features, Mel-Frequency Cepstral Coefficients (MFCC), is addressed in the framework of Augmented Multiparty Interaction (AMI) corpus, a multi-party and spontaneous speech set of recordings. Both sets of features are independently modeled using mixture of Gaussians and fused together at the score likelihood level. The experiments carried out on the AMI corpus show that incorporating jitter and shimmer measurements to the baseline spectral features decreases the diarization error rate in most of the recordings.Peer ReviewedPostprint (published version

From time series to complex networks: the visibility graph

In this work we present a simple and fast computational method, the visibility algorithm, that converts a time series into a graph. The constructed graph inherits several properties of the series in its structure. Thereby, periodic series convert into regular graphs, and random series do so into random graphs. Moreover, fractal series convert into scale-free networks, enhancing the fact that power law degree distributions are related to fractality, something highly discussed recently. Some remarkable examples and analytical tools are outlined in order to test the method's reliability. Many different measures, recently developed in the complex network theory, could by means of this new approach characterize time series from a new point of view

Iterative pseudo-forced alignment by acoustic CTC loss for self-supervised ASR domain adaptation

High-quality data labeling from specific domains is costly and human time-consuming. In this work, we propose a self-supervised domain adaptation method, based upon an iterative pseudo-forced alignment algorithm. The produced alignments are employed to customize an end-to-end Automatic Speech Recognition (ASR) and iteratively refined. The algorithm is fed with frame-wise character posteriors produced by a seed ASR, trained with out-of-domain data, and optimized throughout a Connectionist Temporal Classification (CTC) loss. The alignments are computed iteratively upon a corpus of broadcast TV. The process is repeated by reducing the quantity of text to be aligned or expanding the alignment window until finding the best possible audio-text alignment. The starting timestamps, or temporal anchors, are produced uniquely based on the confidence score of the last aligned utterance. This score is computed with the paths of the CTC-alignment matrix. With this methodology, no human-revised text references are required. Alignments from long audio files with low-quality transcriptions, like TV captions, are filtered out by confidence score and ready for further ASR adaptation. The obtained results, on both the Spanish RTVE2022 and CommonVoice databases, underpin the feasibility of using CTC-based systems to perform: highly accurate audio-text alignments, domain adaptation and semi-supervised training of end-to-end ASR.Comment: 5 pages, 4 figures, IberSPEECH202