Stefano Fasciani

In this article we describe a user-driven adaptive method to control the sonic response of digital musical instruments using information extracted from the timbre of the human voice. The mapping between heterogeneous attributes of the input and output timbres is determined from data collected through machine-listening techniques and then processed by unsupervised machine-learning algorithms. This approach is based on a minimum-loss mapping that hides any synthesizer-specific parameters and that maps the vocal interaction directly to perceptual characteristics of the generated sound. The mapping adapts to the dynamics detected in the voice and maximizes the timbral space covered by the sound synthesizer. The strategies for mapping vocal control to perceptual timbral features and for automating the customization of vocal interfaces for different users and synthesizers, in general, are evaluated through a variety of qualitative and quantitative methods.

A hybrid controller is designed and tested in simulation-based experiments, overcoming several research challenges associated with stability and control of UAV flight in a GPS-denied environment, achieving faster more accurate autonomous indoor flight. There exists a need for design and modeling of a robust, innovative flight controller that offer more stable, autonomous flight in challenging indoor environments. Drone drifts caused by air turbulence pose as a significant challenge for autonomous aerial systems that are GPS-denied. A hybrid flight controller, that utilizes PID, PIDD 2 and FLC techniques in addition to KF, EKF and Complimentary Error Minimization algorithms, has been developed in a simulation software and validated. Major contributions of the work include: multiple switchable flight modes at the Position and Tracking Controller (PTC) level, and multiple quadcopter flying configuration at the Motor Mixer level. A novel drift correction mechanism utilizes the drone states and an EKF estimator generates a compensation signal with integration of the PTC. FLC adds an extra layer of control by determining the drone's flight mode and flying configuration for the optimal flight output and stability performance. Contributions of this work may further offer increase in accuracy for connected processes enabling UAV flight, including SLAM, path planning, collision detection and avoidance and, subsequently, overall flight performance. 1

This article introduces a granular synthesis algorithm in which grains of sound are processed in the frequency domain and combined at spectral level. In particular, each grain contributes to the sound synthesis with its magnitude spectrum only. Phase is reconstructed using spectrogram inversion techniques which support real-time computation. With this method, windowing and overlapping grains of regular size is no longer required. The algorithm presents a large number of parameters available for the dynamic manipulation of the synthetic timbre. Algorithm implementations are detailed in the paper, including comparison with traditional time-domain against the proposed frequency-domain granula-tion strategies.

We propose an approach to insert physical objects in audio digital signal processing chains, filtering the sound with the acoustic impulse response of any solid measured in real-time. We model physical objects as a linear time-invariant system, which is used as an audio filter. By interacting with the object or with the measuring hardware we can dynamically modify the characteristics of the filter. The impulse response is obtained correlating a noise signal injected in the object through an acoustic actuator with the signal received from an acoustic sensor placed on the object. We also present an efficient multichannel implementation of the system, which enables further creative applications beyond audio filtering, including tangible signal patching and sound spatialization.

Synthesis algorithms often have a large number of adjustable parameters that determine the generated sound and its resultant psychoacoustic features. The relationship between parameters and timbre is important for end users, but it is generally unknown, complex, and difficult to analytically derive. In this paper we introduce a strategy for the analysis of the sonic response of synthesizers subject to the variation of an arbitrary set of parameters. We use an extensive set of sound descriptors which are ranked using a novel metric based on statistical analysis. This enables the study of how changes to a synthesis parameter affect timbral descriptors, and provides a multidimensional model for the mapping of the synthesis control through specific timbre spaces. The analysis, modeling and mapping are integrated in the Timbre Space Analyzer & Mapper (TSAM) tool, which enables further investigation into synthesis sonic response and on perceptually related sonic interactions.

Expressive sonic interaction with sound synthesizers requires the control of a continuous and high dimensional space. Further, the relationship between synthesis variables and timbre of the generated sound is typically complex or unknown to users. In previous works, we presented an unsupervised mapping method based on machine listening and machine learning techniques, which addresses these challenges by providing a low-­‐ dimensional and perceptually related timbre control space. The mapping maximizes the breadth of the ex-­‐ plorable sonic space covered by the sound synthesizer, and minimizes possible timbre losses due to the low-­‐ dimensional control. The mapping is generated automatically by a system requiring little input from users. In this paper we present an improved method and an optimized implementation that drastically reduce the time for timbre analysis and mapping computation. Here we introduce the use of the extreme learning machines for the regression from control to timbre spaces, and an interactive approach for the analysis of the synthesiz-­‐ er sonic response, performed as users explore the parameters of the instrument. This work is implemented in a generic and open-­‐source tool that enables the computation of ad hoc synthesis mappings through timbre spaces, facilitating and speeding up the workflow to get a customized sonic control system.

In this chapter, we discuss a computational method that supports the design and assessment of e-commerce web interfaces by finding the optimal placement of interactive elements. The definition of optimal placement is context-specific and it aims at maximizing measurable aspects of the user experience. We compute the visual model of the web interface using unsupervised image processing techniques which extract features from the browser’s rendering only. In particular, the method we describe segments the interface into different elements, and then it combines pattern matching, character recognition, and colour histogram analysis to compute a model of the interface layout. The model includes information on type and relative position of all elements found in the GUI. Thereafter, the computed model is verified against a set of design rules specified using a simple syntax we designed. This comparison provides a set of recommendations on how to modify the interface to increase the similarity with the reference model, which aims at maximizing specific aspects of the user experience. When multiple design rules exist, we select the appropriate set by classifying the e-commerce website using a text-based technique. To prove this concept, we present a study on e-commerce websites where placement of the checkout button has a significant impact on the online sale process conversion rate. The system identifies non-optimal placement, and it recommends an alternative position that will improve the conversion rate. The system is implemented as an open-source software, and it currently supports the re-positioning of a single interactive element. In the chapter, we discuss a further generalization of this approach to support a larger number of interactive objects in a wider spectrum of scenarios.