A Semantic Web Annotation Tool for a Web-Based Audio Sequencer

Music and sound have a rich semantic structure which is so clear to the composer and the listener, but that remains mostly hidden to computing machinery. Nevertheless, in recent years, the introduction of software tools for music production have enabled new opportunities for migrating this knowledge from humans to machines. A new generation of these tools may exploit sound samples and semantic information coupling for the creation not only of a musical, but also of a "semantic" composition. In this paper we describe an ontology driven content annotation framework for a web-based audio editing tool. In a supervised approach, during the editing process, the graphical web interface allows the user to annotate any part of the composition with concepts from publicly available ontologies. As a test case, we developed a collaborative web-based audio sequencer that provides users with the functionality to remix the audio samples from the Freesound website and subsequently annotate them. The annotation tool can load any ontology and thus gives users the opportunity to augment the work with annotations on the structure of the composition, the musical materials, and the creator's reasoning and intentions. We believe this approach will provide several novel ways to make not only the final audio product, but also the creative process, first class citizens of the Semantic We

Smartphone-based 3D real-time vision system for teleoperation

We present a small form factor 3D vision system that can easily be mounted in any system for teleoperation and presents a low-latency suitable to perform interactive control. The proposed system is based on a commercial 3D smartphone that integrates a stereoscopic camera and a wireless connection. The smartphone has been customized to acquire and transmit stereoscopic video in real-time by means of a special purpose software that runs on the Android operating system. We believe that the choice of a compact solution based on an open source framework and commercial off-the-shelf hardware, will promote the widespread adoption of this architecture by interested developers in multiple scenario

Performance vs Quality of Experience in a remote control application based on real-time 3D video feedback

This paper presents a preliminary study that relates the ability in performing remote control tasks with 3D video feedback and the users' quality of experience (QoE) when users are asked to judge the video quality only. The QoE has been evaluated by means of traditional subjective quality experiments, while an objective performance indicator (alignment error) has been used in the remote control task. Results show that, unexpectedly, even a relatively poor subjective video quality still provides sufficient 3D perception to achieve satisfactory alignment performanc

Estimating packet loss rate in the access through application-level measurements

End user monitoring of quality of experience is one of the necessary steps to achieve an effective and winning control over network neutrality. The involvement of the end user, however, requires the development of light and user-friendly tools that can be easily run at the application level with limited effort and network resources usage. In this paper, we propose a simple model to estimate packet loss rate perceived by a connection, by round trip time and TCP goodput samples collected at the application level. The model is derived from the well-known Mathis equation, which predicts the bandwidth of a steady-state TCP connection under random losses and delayed ACKs and it is evaluated in a testbed environment under a wide range of different conditions. Experiments are also run on real access networks. We plan to use the model to analyze the results collected by the "network neutrality bot" (Neubot), a research tool that performs application-level network-performance measurements. However, the methodology is easily portable and can be interesting for basically any user application that performs large downloads or uploads and requires to estimate access network quality and its variation

Discovering users with similar internet access performance through cluster analysis

Users typically subscribe to an Internet access service on the basis of a specific download speed, but the actual service may differ. Several projects are active collecting internet access performance measurements on a large scale at the end user location. However, less attention has been devoted to analyzing such data and to inform users on the received services. This paper presents MiND, a cluster-based methodology to analyze the characteristics of periodic Internet measurements collected at the end user location. MiND allows to discover (i) groups of users with a similar Internet access behavior and (ii) the (few) users with somehow anomalous service. User measurements over time have been modeled through histograms and then analyzed through a new two-level clustering strategy. MiND has been evaluated on real data collected by Neubot, an open source tool, voluntary installed by users, that periodically collects Internet measurements. Experimental results show that the majority of users can be grouped into homogeneous and cohesive clusters according to the Internet access service that they receive in practice, while a few users receiving anomalous services are correctly identified as outliers. Both users and ISPs can benefit from such information: users can constantly monitor the ISP offered service, whereas ISPs can quickly identify anomalous behaviors in their offered services and act accordingly

Investigating Prediction Accuracy of Full Reference Objective Video Quality Measures through the ITS4S Dataset

Large subjectively annotated datasets are crucial to the development and testing of objective video quality measures (VQMs). In this work we focus on the recently released ITS4S dataset. Relying on statistical tools, we show that the content of the dataset is rather heterogeneous from the point of view of quality assessment. Such diversity naturally makes the dataset a worthy asset to validate the accuracy of video quality metrics (VQMs). In particular we study the ability of VQMs to model the reduction or the increase of the visibility of distortion due to the spatial activity in the content. The study reveals that VQMs are likely to overestimate the perceived quality of processed video sequences whose source is characterized by few spatial details. We then propose an approach aiming at modeling the impact of spatial activity on distortion visibility when objectively assessing the visual quality of a content. The effectiveness of the proposal is validated on the ITS4S dataset as well as on the Netflix public dataset

Mapping WiFi measurements on OpenStreetMap data for Wireless Street Coverage Analysis

The growing interest on smart cities and the deployment of an ever increasing number of smart objects in public locations, such as dumpsters, traffic lights, and manholes, requires ubiquitous connectivity for these devices to communicate data and to receive configurations. Opportunistic WiFi connectivity is a valid alternative both to ad hoc solutions, like LoRa, which require costly deployments, and to communicating through the mobile network, which is both pricey and battery power hungry. In this paper we present a tool to analyze the WiFi coverage of home Access Points (AP) on the city streets. It can be of interest to ISP or other providers which want to offer connectivity to Internet of Things smart objects deployed around the city. We describe a method for gathering WiFi measures around the city (by leveraging crowdsourcing) and an open source visualization and analysis web application to explore the accumulated data. More importantly, this framework can leverage the semantic information contained in OpenStreetMap data to extract further knowledge about the AP deployment in the city, for example we investigate the relationship between the AP density per square kilometer within the city and the WiFi street coverage ratio

Experiencing Remote Classical Music Performance Over Long Distance: A JackTrip Concert Between Two Continents During the Pandemic

The recent lockdown restrictions imposed by the severe acute respiratory syndrome coronavirus 2 pandemic have heightened the need for new forms of remote collaboration for music schools, conservatories, musician ensembles, and artists, each of which would benefit from being provided with adequate tools to make high-quality, live collaborative music in a distributed fashion. This paper demonstrates the usage of the Networked Music Performance software JackTrip to support a distributed classical concert involving singers and musicians from four different locations in two continents, using readily available hardware/software solutions and internet connections while guaranteeing high-fidelity audio quality. This paper provides a description of the technical setup with a numerical analysis of the achieved mouth-to-ear latency and assessment of the music-making experience as perceived by the performers