GFM-Voc: A real-time voice quality modification system

International audienc

Modeling, Simulation and Data Processing for Additive Manufacturing

Additive manufacturing (AM) or, more commonly, 3D printing is one of the fundamental elements of Industry 4.0. and the fourth industrial revolution. It has shown its potential example in the medical, automotive, aerospace, and spare part sectors. Personal manufacturing, complex and optimized parts, short series manufacturing and local on-demand manufacturing are some of the current benefits. Businesses based on AM have experienced double-digit growth in recent years. Accordingly, we have witnessed considerable efforts in developing processes and materials in terms of speed, costs, and availability. These open up new applications and business case possibilities all the time, which were not previously in existence. Most research has focused on material and AM process development or effort to utilize existing materials and processes for industrial applications. However, improving the understanding and simulation of materials and AM process and understanding the effect of different steps in the AM workflow can increase the performance even more. The best way of benefit of AM is to understand all the steps related to that—from the design and simulation to additive manufacturing and post-processing ending the actual application.The objective of this Special Issue was to provide a forum for researchers and practitioners to exchange their latest achievements and identify critical issues and challenges for future investigations on “Modeling, Simulation and Data Processing for Additive Manufacturing”. The Special Issue consists of 10 original full-length articles on the topic

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial

OPENGLOT – An open environment for the evaluation of glottal inverse filtering

Glottal inverse filtering (GIF) refers to technology to estimate the source of voiced speech, the glottal flow, from speech signals. When a new GIF algorithm is proposed, its accuracy needs to be evaluated. However, the evaluation of GIF is problematic because the ground truth, the real glottal volume velocity signal generated by the vocal folds, cannot be recorded non-invasively from natural speech. This absence of the ground truth has been circumvented in most previous GIF studies by using simple linear source-filter synthesis techniques with known artificial glottal flow models and all-pole vocal tract filters. Moreover, in a few previous studies, physical modeling of speech production has been utilized in synthesis of the test data for GIF evaluation. The evaluation strategy in previous GIF studies is, however, scattered between individual investigations and there is currently a lack of a coherent, common platform to be used in GIF evaluation. In order to address this shortcoming, the current study introduces a new environment, called OPENGLOT, for GIF evaluation. The key ideas of OPENGLOT are twofold: the environment is versatile (i.e., it provides different types of test signals for GIF evaluation) and open (i.e., the system can be used by anyone who wants to evaluate her or his new GIF method and compare it objectively to previously developed benchmark techniques). OPENGLOT consists of four main parts, Repositories I–IV, that contain data and sound synthesis software. Repository I contains a large set of synthetic glottal flow waveforms, and speech signals generated by using the Liljencrants–Fant (LF) waveform as an artificial excitation, and a digital all-pole filter to model the vocal tract. Repository II contains glottal flow and speech pressure signals generated using physical modeling of human speech production. Repository III contains pairs of glottal excitation and speech pressure signal generated by exciting 3D printed plastic vocal tract replica with LF excitations via a loudspeaker. Finally, Repository IV contains multichannel recordings (speech pressure signal, electroglottogram, high-speed video of the vocal folds) from natural production of speech. After presenting these four core parts of OPENGLOT, the article demonstrates the platform by presenting a typical use case.Peer reviewe