A Time-Frequency Generative Adversarial based method for Audio Packet Loss Concealment

Packet loss is a major cause of voice quality degradation in VoIP transmissions with serious impact on intelligibility and user experience. This paper describes a system based on a generative adversarial approach, which aims to repair the lost fragments during the transmission of audio streams. Inspired by the powerful image-to-image translation capability of Generative Adversarial Networks (GANs), we propose bin2bin, an improved pix2pix framework to achieve the translation task from magnitude spectrograms of audio frames with lost packets, to noncorrupted speech spectrograms. In order to better maintain the structural information after spectrogram translation, this paper introduces the combination of two STFT-based loss functions, mixed with the traditional GAN objective. Furthermore, we employ a modified PatchGAN structure as discriminator and we lower the concealment time by a proper initialization of the phase reconstruction algorithm. Experimental results show that the proposed method has obvious advantages when compared with the current state-of-the-art methods, as it can better handle both high packet loss rates and large gaps.Comment: Accepted at EUSIPCO - 31st European Signal Processing Conference, 202

Tackling the Linear Sum Assignment Problem with Graph Neural Networks

Linear Assignment Problems are fundamental combinatorial optimization problems that appear throughout domains such as logistics, robotics and telecommunications. In general, solving assignment problems to optimality is computationally infeasible even for contexts of small dimensionality, and so heuristic algorithms are often employed to find near-optimal solutions. The handcrafting of a heuristic usually requires expert-knowledge to exploit the problem structure to be addressed, however if the problem description changes slightly, a previously derived heuristic may no longer be appropriate. This work explores a more general-purpose learning approach, based on the description of the problem through a bipartite graph, and the use of a Message Passing Graph Neural Network model, to attain the correct assignment permutation. The simulation results indicate that the proposed structure allows for a significant increase in classification accuracy if compared with two different DNN approaches based on Dense Networks and Convolutional Neural Networks, furthermore, the GNN has proved to be very efficient with regard to the processing time and memory requirements, thanks to intrinsic parameter-sharing capability

Die Ballistokardiographie misst die Herzfrequenz anhand der mechanischen Körperschwingungen aus der Herzbewegung

Für die Überwachung des Schlafs zu Hause sind nichtinvasive Methoden besonders gut anwendbar. Die Signale, die häufig überwacht werden, sind Herzfrequenz und Atemfrequenz. Die Ballistokardiographie (BCG)ist eine Technik, bei der die Herzfrequenz aus den mechanischen Schwingungen des Körpers bei jedem Herzzyklus gemessen wird. Kürzlich wurden Übersichtsarbeiten veröffentlicht. Die Untersuchung soll in einem ersten Ansatz bewerten, ob die Herzfrequenz anhand von BCG erkannt werden kann. Die wesentlichen Randbedingungen sind, ob dies gelingt, wenn der Sensor unter der Matratze positioniert wird und kostengünstige Sensoren zum Einsatz kommen

Heart rate detection with accelerometric sensors under the mattress

The ballistocardiography is a technique that measures the heart rate from the mechanical vibrations of the body due to the heart movement. In this work a novel noninvasive device placed under the mattress of a bed estimates the heart rate using the ballistocardiography. Different algorithms for heart rate estimation have been developed