Exploring complex rhythmic devices in new music composition through software design

This thesis examines the role that complex rhythms perform in my music. I will demonstrate how the software I have created is unique and necessary for this type of rhythmic exploration in music composition and how it differs from existing softwares. I will investigate the practice of hearing one’s environment as music and how the development of my software and compositions are integrally linked to this phenomenon and make clear the importance of advanced rhythmic study within this practice. I am particularly interested in extending my own and others’ perceptual capabilities to hear more and more complex rhythms accurately and congruently with what they would normally consider ‘groove’. To this end, my project involves the development of softwares that: mathematically model the naturally occurring rhythms of specific species of frogs; allow the simultaneous occurrence of ninety-six different tempos; explore Miles Okazaki’s Rhythm Matrix; enable the creation of new and complex grooves from simple beginnings via performance means; allow for infinitely complex variable mapping of musical parameters and rhythms via simple gestural controls; are completely modular and dynamic in design, thereby freeing the user from normal software design limitations. I will demonstrate the use of these softwares in my music compositions and analyse the compositions from within the context of rhythmic exploration and discovery. KEYWORDS: composition, computer music, software, polyrhythm, algorithmic, experimental, groove, tempo, beat, poly-tempi, nature, frog

Flocking: A framework for declarative music-making on the Web

(Abstract to follow

Financial news analysis using a semantic web approach

In this paper we present StockWatcher, an OWL-based web application that enables the extraction of relevant news items from RSS feeds concerning the NASDAQ-100 listed companies. The application's goal is to present a customized, aggregated view of the news categorized by different topics. We distinguish between four relevant news categories: i) news regarding the company itself, ii) news regarding direct competitors of the company, iii) news regarding important people of the company, and iv) news regarding the industry in which the company is active. At the same time, the system presented in this chapter is able to rate these news items based on their relevance. We identify three possible effects that a news message can have on the company, and thus on the stock price of that company: i) positive, ii) negative, and iii) neutral. Currently, StockWatcher provides support for the NASDAQ-100 companies. The selection of the relevant news items is based on a customizable user portfolio that may consist of one or more of these companies

H-SLAM: Hybrid Direct-Indirect Visual SLAM

The recent success of hybrid methods in monocular odometry has led to many attempts to generalize the performance gains to hybrid monocular SLAM. However, most attempts fall short in several respects, with the most prominent issue being the need for two different map representations (local and global maps), with each requiring different, computationally expensive, and often redundant processes to maintain. Moreover, these maps tend to drift with respect to each other, resulting in contradicting pose and scene estimates, and leading to catastrophic failure. In this paper, we propose a novel approach that makes use of descriptor sharing to generate a single inverse depth scene representation. This representation can be used locally, queried globally to perform loop closure, and has the ability to re-activate previously observed map points after redundant points are marginalized from the local map, eliminating the need for separate and redundant map maintenance processes. The maps generated by our method exhibit no drift between each other, and can be computed at a fraction of the computational cost and memory footprint required by other monocular SLAM systems. Despite the reduced resource requirements, the proposed approach maintains its robustness and accuracy, delivering performance comparable to state-of-the-art SLAM methods (e.g., LDSO, ORB-SLAM3) on the majority of sequences from well-known datasets like EuRoC, KITTI, and TUM VI. The source code is available at: https://github.com/AUBVRL/fslam_ros_docker