Leonardo Gabrielli

Multi-resolution linear prediction based features for audio onset detection with bidirectional LSTM neural networks

A plethora of different onset detection methods have been proposed in the recent years. However, few attempts have been made with respect to widely-applicable approaches in order to achieve superior performances over different types of music and with considerable temporal precision. In this paper, we present a multi-resolution approach based on discrete wavelet transform and linear prediction filtering that improves time resolution and performance of onset detection in different musical scenarios. In our approach, wavelet coefficients and forward prediction errors are combined with auditory spectral features and then processed by a bidirectional Long Short-Term Memory recurrent neural network, which acts as reduction function. The network is trained with a large database of onset data covering various genres and onset types. We compare results with state-of-the-art methods on a dataset that includes Bello, Glover and ISMIR 2004 Ballroom sets, and we conclude that our approach significantly outperforms existing methods in terms of F-Measure. For pitched non percussive music an absolute improvement of 7.5% is reported.

Wireless M-Bus Sensor Networks for Smart Water Grids: Analysis and Results

Wireless sensor network technologies are experiencing an impressive development, as they represent one of the building blocks upon which new paradigms, such as Internet of Things and Smart Cities, may be implemented. Among the different applications enabled by such technologies, automatic monitoring of the water grid, and smart metering of water consumptions, may have a great impact on the preservation of one of the most valued, and increasingly scarce, natural resources. Sensor nodes located along water pipes cannot rely on the availability of power grid facilities to get the necessary energy imposed by their working conditions. In this sense, an energy-efficient design of the network architecture, and the evaluation of Energy Harvesting techniques to sustain its nodes, becomes of paramount importance. This paper investigates the suitability of a Wireless Metering Bus-based solution for the implementation of smart water grids, by evaluating network and node related performance, through simulations, prototype design, and experimental tests, which confirm the feasibility and efficiency of the proposal.

Wireless M-Bus sensor nodes in smart water grids: The energy issue

Smart Metering is one of the key issues in modern energy efficiency technologies. Several efforts have been recently made in developing suitable communication protocols for metering data management and transmission, and the Metering-Bus (M-Bus) is a relevant standard example, with a wide diffusion in the European market. This paper deals with its wireless evolution, namely Wireless M-Bus (WM-Bus), and in particular looks at it from the energy consumption perspective. Indeed, specially in those applicative scenarios where the grid powering is not available, like in water and gas metering settings, it is fundamental to guarantee the sustainability of the meter itself, by means of long-life batteries or suitable energy harvesting technologies. The present work analyzes all these aspects directly referring to a specific HW/SW implementation of the WM-Bus variants, providing some useful guidelines for its application in the smart water grid context.

Computational Intelligence in Smart water and gas grids: An up-to-date overview

Computational Intelligence plays a relevant role in several Smart Grid applications, and there is a florid literature in this regard. However, most of the efforts have been oriented to the electrical energy field, for which many contributions have appeared so far, also facilitated by the availability of suitable databases to use for system training and testing. Different is the case for the water and gas scenarios: this work is thus oriented to present the state-of-the-art techniques for these grids, from 2009 to date. In particular, the focus is on load forecasting and leakage detection applications, that are the most addressed in the literature and present the biggest interest from a commercial point of view as well: the main characteristics and registered performance for all the reviewed approaches are reported. Along this direction, an extensive search of used databases has been performed and thus made available to the research community.

Networked Beagleboards for wireless music applications

One of the most demanding challenges in the field of audio engineering is the transmission of low-latency high quality audio streams over networks. While several protocols nowadays allow wired local network streaming, much effort is still required to achieve similar goals over existing wireless LAN technologies. While the challenge is still far from being solved, several design issues can be highlighted and future scenarios can be outlined. This paper proposes the setup of a wireless music production system based on open hardware and open software which requires relatively low setup effort while allowing for a high flexibility of use. The hardware platform is the Beagleboard, based on Texas Instruments DM3730, running a GNU/Linux OS and the computer music language Pure Data. Such a device can capture electric instrument audio, generate sound, send MIDI or OSC control data, and stream to PCs and other embedded devices operating as mixers, effect racks and so on, enabling an ecosystem of flexible and open devices. Tests conducted on a home wireless network show acceptable latency for many applications.

Reducing the latency in live music transmission with the BeagleBoard xM through resampling

In previous works, a widespread embedded platform, the BeagleBoard xM, was shown to provide sufficient through-put and acceptable latency for live music control and audio signals transmission on standard LAN and WLAN protocols. Although the preliminary work opened the way for further investigations, the software stack did not prove efficient enough to deliver stable audio performance below the perceptual latency threshold for good ensemble playing. This work reports on a custom Debian Linux image, called WeMUST-OS for the BeagleBoard xM, configured for the task at hand by careful low-level ALSA driver configuration with the DM3730 SoC and the TPS65950 audio codec to improve local audio input/output latency. Furthermore Jacktrip, an application for music audio transmission, has been modified with added support to resampling enabling seamless connection with other devices running audio at different sample rate and period size. Leveraging these two achievements, latency is shown to keep under perceptual threshold for ensemble music performance including transmission of the monitoring signal.

A digital waveguide-based approach for Clavinet modeling and synthesis

The Clavinet is an electromechanical musical instrument produced in the mid-twentieth century. As is the case for other vintage instruments, it is subject to aging and requires great effort to be maintained or restored. This paper reports analyses conducted on a Hohner Clavinet D6 and proposes a computational model to faithfully reproduce the Clavinet sound in real time, from tone generation to the emulation of the electronic components. The string excitation signal model is physically inspired and represents a cheap solution in terms of both computational resources and especially memory requirements (compared, e.g., to sample playback systems). Pickups and amplifier models have been implemented which enhance the natural character of the sound with respect to previous work. A model has been implemented on a real-time software platform, Pure Data, capable of a 10-voice polyphony with low latency on an embedded device. Finally, subjective listening tests conducted using the current model are compared to previous tests showing slightly improved results.

Domestic Water and Natural Gas Demand Forecasting by Using Heterogeneous Data: A Preliminary Study

In this paper a preliminary study concerning prediction of domestic consumptions of water and natural gas based on genetic programming (GP) and its combination with extended Kalman filter (EKF) is presented. The used database (AMPds) are composed of power, water, natural gas consumptions and temperatures. The study aims to investigate novel solutions and adopts state-of-the-art approaches to forecast resource demands using heterogeneous data of an household scenario. In order to have a better insight of the prediction performance and properly evaluate possible correlation between the various data types, the GP approach has been applied varying the combination of input data, the time resolution, the number of previous data used for the prediction (lags) and the maximum depth of the tree. The best performance for both water and natural gas prediction have been achieved using the results obtained by the GP model created for a time resolution of 24 h, and using a set of input data composed of both water and natural gas consumptions. The results confirm the presence of a strong correlation between natural gas and water consumptions. Additional experiments have been executed in order to evaluate the effect of the prediction performance using long period heterogeneous data, obtained from the U.S. Energy Information Administration (E.I.A.).

A nonlinear second-order digital oscillator for Virtual Acoustic Feedback

The guitar feedback effect, or howling, is well known to the general public and identified with many rock music genres and it is the only case of acoustic feedback employed for musical purposes. Virtual Acoustic Feedback (VAF), is regarded as the extension of this phenomenon to any instrument or sound source by means of virtual acoustics and is meant to enrich the sound palette of a musician. The study of the acoustic feedback as a musical tool and computational techniques for its emulation have been scarcely addressed in literature. In this paper a nonlinear feedback oscillator is proposed and its properties derived. The oscillator does not necessarily need to be connected to a virtual instrument, thus enables to process any kind of pitched real-time input.

Wireless Networked Music Performance

Wireless NMP has very few examples in the literature, if none (depending on the definition the reader adopts for NMP). This chapter reports advancements and developments in wireless NMP. The challenges posed by wireless NMP and the opportunities it offers are different from those seen in wired remote NMP. For this reason, a specific approach and framework has been developed by the authors which is reported in this chapter and compared to other meaningful approaches and technical achievements from other authors in wireless and wired NMP. Most of the contributions are by the authors and colleagues. The rationale and goals of the authors’ project, named WeMUST, are described and its technical achievements later reported. The project also targets portability and ease of use in wireless NMP. Embedded platforms are, thus, employed which are power-autonomous and provide some DSP capabilities. They adopt connection automation tools based on custom service discovery mechanisms based on existing networking technologies. The software used and related parameters are described and motivated. Finally, issues related to outdoor use are reported and technical choices to overcome these are described.