The advent of open-source serverless computing frameworks has introduced the ability to bring the Functions-as-a-Service (FaaS) paradigm for applications to be executed on-premises. In particular, data-driven scientific applications can benefit from these frameworks with the ability to trigger scalable computation in response to incoming workloads of files to be processed. This paper introduces an open-source framework to achieve on-premises serverless computing for event-driven data processing applications that features: i) the automated provisioning of an elastic Kubernetes cluster that can grow and shrink, in terms of the number of nodes, on multi-Clouds; ii) the automated deployment of a FaaS framework together with a data storage back-end that triggers events upon file uploads; iii) a service that provides a REST API to orchestrate the creation of such functions and iv) a graphical user interface that provides a unified entry point to interact with the aforementioned services. Together, this provides a framework to deploy a computing platform to create highly-parallel event-driven file-processing serverless applications that execute on customized runtime environments provided by Docker containers that run on an elastic Kubernetes cluster. The usefulness of this framework is exemplified by means of the execution of a data-driven workflow for optimised object detection on video. The workflow is tested under three different workloads which process ten, a hundred and a thousand functions. The results show that the presented architecture is able to process such workloads taking advantage of its elasticity to make a sensible usage of the resources.

There is an opportunity for Distributed Computing Infrastructures (DCIs) to embrace container-based virtualisation to support efficient execution of scientific applications without the performance penalty commonly introduced by Virtual Machines (VMs). However, containers (e.g. Docker) and VMs feature different image formats and disparate procedures for deployment and management, thus hindering the adoption of hybrid DCIs (HDCIs) comprised of those kind of resources. This paper describes a workflow based on open-source tools and standards to introduce coherent application delivery on HDCIs in which applications require to be deployed on both VMs and Docker containers. Leveraging and extending the TOSCA standard to describe application requirements, and adopting DevOps practices, resulted in the coherent creation of the artifacts required for the execution of the applications on different platforms. The paper features the adoption of this approach in the INDIGO-DataCloud project.

Acquisition of instrumental gestures in musical performances is a field of increasing interest with applications in different areas ranging from acoustics and sound synthesis to motor learning or artistic performances. Direct acquisition approaches are based on measurements with sensors attached on the instrument or the performer, a process that usually involves the use of expensive sensing systems and complex setups that are generally intrusive in practice. An alternative is the indirect acquisition without sensors based on analysis of the audio signal. This paper reports a novel indirect acquisition method for the estimation of continuous violin controls from audio-signal analysis based on the training of statistical models with a database of previously recorded violin performances. The database contains synchronized streams of audio features and instrumental controls. Audio signal was captured with a vibration transducer built into the violin bridge, and instrumental controls were measured with sensors. The controls include bowing parameters (played string, bowing velocity, bowing force, and bowing distance to the bridge) as well as fingering position. Once the model is trained for a specific violin, we can perform indirect acquisition from analysis of the signal captured with its embedded transducer without the need for the sensors any more. The statistical methods used are Hidden Markov Models (HMM) with observation distributions parameterized as Multivariate Gaussian Mixtures (GM). HMMs provide a means for note recognition and following and parameter prediction is based on GM regression. Results show that the presented method constitutes an accurate, non-intrusive and low-cost alternative for instrumental control acquisition of a previously calibrated violin and recording device.

This work presents a method to compute violin body impulse responses (BIR) of acoustic violins that are adapted to the signal captured with an electric violin (a violin with a transducer measuring vibration). The computed BIRs correspond to the transfer function that maps the pickup signal of the electric violin to the radiated acoustic sound of the acoustic violins. By convolution of the pickup signal with the corresponding BIR, we pretend to simulate the radiated sound of the acoustic violin by playing the electric one. The method to obtain the BIRs is based on signal deconvolution between a recorded audio signal of an acoustic violin and the signal coming from an electric violin's pickup. The recorded signals consist of glissandi performed with a violin-playing machine enhanced with motion sensors that provide the bowing parameters (bowing position, velocity and force). By controlling the bowing parameters and analyzing the fundamental frequency of each frame we perform the deconvolution on equivalent frames and obtain the desired impulse response between the two instruments. A user survey consisting of violinists and non-violinists was performed to evaluate the obtained results with respect to the original pickup signal.

The objective of this research is to model the relationship between actions performed by a violinist and the sound which these actions produce. Violinist actions and audio are captured during real performances by means of a newly developed sensing system from which bowing and audio descriptors are computed. A database is built with this data and used to train a generative model based on neural networks. The model is driven by a continuous sequence of bowing and fingering controls and is able to generate their corresponding sequence of spectral envelopes. The model is used for synthesis, either alone as a purely spectral model, by filling the predicted envelopes with harmonic and noisy components, or coupled with a concatenative synthesizer, where the predicted envelopes are used as time-varying filters to transform the concatenated samples. The combination of sample concatenation with the timbre model allows for the preservation of sound quality inherent in samples, while providing a high level of control. Additionally, we perform an analysis of the violinist control space and the influence of the controls on the timbre.

Professional musicians intuitively manipulate sound properties such as pitch, timing, amplitude and timbre in order to produce expressive performances of particular pieces. However, there is little explicit information about how and in which musical

Excitation-continuous music instrument control patterns are often not explicitly represented in current sound synthesis techniques when applied to automatic performance. Both physical model-based and sample-based synthesis paradigms would benefit from a flexible and accurate instrument control model, enabling the improvement of naturalness and realism. We present a framework for modeling bowing control parameters in violin performance. Nearly non-intrusive sensing techniques allow for accurate acquisition of relevant timbre-related bowing control parameter signals. We model the temporal contour of bow velocity, bow pressing force, and bow-bridge distance as sequences of short Be¿zier cubic curve segments. Considering different articulations, dynamics, and performance contexts, a number of note classes are defined. Contours of bowing parameters in a performance database are analyzed at note-level by following a predefined grammar that dictates characteristics of curve segment sequences for each of the classes in consideration. As a result, contour analysis of bowing parameters of each note yields an optimal representation vector that is sufficient for reconstructing original contours with significant fidelity. From the resulting representation vectors, we construct a statistical model based on Gaussian mixtures suitable for both the analysis and synthesis of bowing parameter contours. By using the estimated models, synthetic contours can be generated through a bow planning algorithm able to reproduce possible constraints caused by the finite length of the bow. Rendered contours are successfully used in two preliminary synthesis frameworks: digital waveguide-based bowed string physical modeling and sample-based spectral-domain synthesis.