Latent representations for traditional music analysis and generation

We create and study a generative model for Irish traditional music based on Variational Autoencoders and analyze the learned latent space trying to find musically significant correlations in the latent codes' distributions in order to perform musical analysis on data. We train two kinds of models: one trained on a dataset of Irish folk melodies, one trained on bars extrapolated from the melodies dataset, each one in five variations of increasing size. We conduct the following experiments: we inspect the latent space of tunes and bars in relation to key, time signature, and estimated harmonic function of bars; we search for links between tunes in a particular style (i.e. "reels'") and their positioning in latent space relative to other tunes; we compute distances between embedded bars in a tune to gain insight into the model's understanding of the similarity between bars. Finally, we show and evaluate generative examples. We find that the learned latent space does not explicitly encode musical information and is thus unusable for musical analysis of data, while generative results are generally good and not strictly dependent on the musical coherence of the model's internal representation.

An optimal control tool for trajectory generation of autonomous drones

In the recent years, autonomous aerial vehicles gained large popularity in a variety of applications in the field of automation. To accomplish various and challenging tasks the capability of generating trajectories has assumed a key role. As higher performances are sought, traditional, flatness-based trajectory generation schemes present their limitations. In these approaches the highly nonlinear dynamics of the quadrotor is, indeed, neglected. Therefore, strategies based on optimal control principles turn out to be beneficial, since in the trajectory generation process they allow the control unit to best exploit the actual dynamics, and enable the drone to perform quite aggressive maneuvers. This dissertation is then concerned with the development of an optimal control technique to generate trajectories for autonomous drones. The algorithm adopted to this end is a second-order iterative method working directly in continuous-time, which, under proper initialization, guarantees quadratic convergence to a locally optimal trajectory. At each iteration a quadratic approximation of the cost functional is minimized and a decreasing direction is then obtained as a linear-affine control law, after solving a differential Riccati equation. The algorithm has been implemented and its effectiveness has been tested on the vectored-thrust dynamical model of a quadrotor in a realistic simulative setup.

Ephemeris generation of the Galilean moons by using VLBI observations in support to stellar occultations

Stellar occultations are the most accurate Earth-based astronomy technique to obtain the lateral position of celestial bodies, in the case of natural satellites, their accuracy also depends on the central body to which the satellite orbits. The main goal of this thesis work is to analyze if and how very long baseline interferometry (VLBI) measurements of a body like Jupiter can be used in support to stellar occultations of its natural satellites by reducing the planetary uncertainty at the time of the occultation. In particular, we analyzed the events of the stellar occultations of Callisto (15.01.2024) and Io (02.04.2021). The stellar occultation of Callisto has been predicted and simulated using the stellar occultation reduction analysis (SORA) toolkit while the stellar occultation of Io has been already studied by Morgado et al. We then simulated the VLBI data of Jupiter according to the current JUNO trajectories. The required observation were then used as input of an estimation to which then we performed a covariance analysis on the estimated parameters to retrieve the formal errors (1 − σ uncertainties) at each epoch of the propagation. The results show that the addition of the VLBI slightly improves the uncertainty of Callisto even when Jupiter knowledge is worse while for Io we observed that the VLBI data is especially crucial in the scenario of an a priori uncertainty in Jupiter state of about 10km. Here we can have improvements of the estimated initial states of Io of about 70m, 230m and 900m to the radial, along-track and cross-track directions respectively. Moreover, we have also obtained the propagated errors of the two moons in terms of right ascension and declination which both show uncertainties in the mas level at the occultation time. Finally, we simulated Io and Europa together and we observed that at the time of the stellar occultation of Europa the along-track component of Io is constrained, confirming the coupling between the two inner moons.