Gestural composition with arbitrary musical objects and dynamic transformation networks

University of Minnesota Ph.D. dissertation. June 2014. Major: Music. Advisors:Guerino Mazzola Co-Advised by Michael Cherlin. 1 computer file (PDF); xii, 256 pages.This thesis proposes a theoretical framework and a working implementation of a music composition software that overcomes many aspects of the distance commonly perceived by composers when using software products dealing with abstract representations of music. It is based on elaborate musical spaces and complex algorithmic procedures (constructs from contemporary mathematical music theory), which it makes more accessible using an intuitive gestural interface. More specifically, the software applies the principles of transformational theory and gesture theory to composition rather than analysis, poiesis rather than esthesis. The overall design of the software is based on the three levels of the ontological dimension of embodiment: the levels of facts, processes, and gestures. Users can interact with any of these levels via the software's graphical user interface. They can define and manipulate arbitrary musical objects in a gestural way and their compositional process is recorded in a transformation graph. This graph, in turn can be modified in a gestural way and finally translated back into standardized gestures that represent the evolution of the musical work

Tracking the Evolution of a Band's Live Performances over Decades

International Society for Music Information Retrieval Conference (ISMIR 2022) , Bengaluru, India, December 4-8, 2022Evolutionary studies have become a dominant thread in the analysis of large audio collections. Such corpora usually consist of musical pieces by various composers or bands and the studies usually focus on identifying general historical trends in harmonic content or music production techniques. In this paper we present a comparable study that examines the music of a single band whose publicly available live recordings span three decades. We first discuss the opportunities and challenges faced when working with single-artist and live-music datasets and introduce solutions for audio feature validation and outlier detection. We then investigate how individual songs vary over time and identify general performance trends using a new approach based on relative feature values, which improves accuracy for features with a large variance. Finally, we validate our findings by juxtaposing them with descriptions posted in online forums by experienced listeners of the band's large following

Towards a Framework for the Discovery of Collections of Live Music Recordings and Artefacts on the Semantic Web

This paper introduces a platform for the representation and discovery of live music recordings and associated artefacts based on a dedicated data model. We demonstrate our technology by implementing a Web-based discovery tool for the Grateful Dead collection of the Internet Archive, a large collection of concert recordings annotated with editorial metadata. We represent this information using a Linked Data model complemented with data aggregated from several additional Web resources discussing and describing these events. These data include descriptions and images of physical artefacts such as tickets, posters and fan photos, as well as other information, e.g. about location and weather. The system uses signal processing techniques for the analysis and alignment of the digital recordings. During the discovery, users can juxtapose and compare different recordings of a given concert, or different performances of a given song by interactively blending between them

Der Wert der chirurgischen Therapie beim hepatisch metastasierten Nierenzellkarzinom im Zeitalter von Tyrosinkinaseinhibitoren

Das Management von Patienten mit einem metastasierten Nierenzellkarzinom (mNZK) befindet sich in ständigem Wandel und erfolgt zunehmend in einem multidisziplinären Ansatz. Die Entwicklung einer zielgerichteten Therapie mittels Tyrosinkinaseinhibitoren (TKIs) stellt einen Meilenstein in der Behandlung dieser Erkrankung dar. Da jedoch eine komplette Remission unter rein medikamentöser Behandlung noch immer selten beobachtet wird, spielt die chirurgische Entfernung des Primärtumors und seiner Metastasen eine wichtige Rolle im multimodalen Behandlungskonzept dieser Patienten. Dies gilt insbesondere dann, wenn ein kurativer Therapieansatz verfolgt wird. Die vorliegende Arbeit analysiert retrospektiv medizinische Daten von 35 Patienten mit einem mNZK, welche bei histologisch gesicherten Lebermetastasen zwischen 1992 und 2015 am Universitätsklinikum Leipzig mittels einer leber-gerichteten Therapie (LDT) behandelt wurden. Bei einem Teil der Patienten (16 Patienten) wurde nach chirurgischer bzw. interventioneller Behandlung der Lebermetastasen eine sofortige TKI-Therapie begonnen. Wir konnten zeigen, dass eine zeitnah postoperativ begonnene Therapie mit TKIs signifikant mit einem verbesserten Gesamtüberleben (OS) korrelierte und zudem eine signifikante Verlängerung der Zeit bis zum Tumorprogress (PFS) zu beobachten war. Daneben konnten in der multivariaten Überlebenszeitanalyse nach Leberresektion die TKI-Therapie, der ECOG-Status (p = 0.001) sowie das Metastasierungsverhalten (p = 0.015) als unabhängige prognostische Parameter identifiziert werden. Diese Ergebnisse stärken die Empfehlung, dass die LDT im Zeitalter der TKI-Therapie bei geeigneten Patienten ein sicheres und zuverlässiges additives Verfahren im multimodalen Behandlungskonzept des mNZK darstellt. Vor dem Hintergrund von neuen und potenteren systemischen Therapieoptionen (TT und Checkpointinhibitortherapie) spielt eine sorgfältige Patientenselektion für eine chirurgische Therapie eine wesentliche Rolle. Dabei könnten klinische und paraklinische Prognosefaktoren wie auch diagnostische/therapeutische Algorithmen helfen, Patienten zu identifizieren, welche von einer chirurgischen Therapie in hohem Maße profitieren

Exploring Musical Expression on the Web: Deforming, Exaggerating, and Blending Decomposed Recordings

We introduce a prototype of an educational web application for comparative performance analysis based on source separation and object-based audio techniques. The underlying system decomposes recordings of classical music performances into note events using score-informed source separation and represents the decomposed material using semantic web technologies. In a visual and interactive way, users can explore individual performances by highlighting specific musical aspects directly within the audio and by altering the temporal characteristics to obtain versions in which the micro-timing is exaggerated or suppressed. Multiple performances of the same work can be compared by juxtaposing and blending between the corresponding recordings. Finally, by adjusting the timing of events, users can generate intermediates of multiple performances to investigate their commonalities and differences

Black Box or Open Science? Assessing Reproducibility-Related Documentation in AI Research

The surge in Artificial Intelligence (AI) research has spurred significant breakthroughs across various fields. However, AI is known for its Black Box character and reproducing AI outcomes challenging. Open Science, emphasizing transparency, reproducibility, and accessibility, is crucial in this context, ensuring research validity and facilitating practical AI adoption. We propose a framework to assess the quality of AI documentation and assess 51 papers. We conclude that despite guidelines, many AI papers fall short on reproducibility due to insufficient documentation. It is crucial to provide comprehensive details on training data, source code, and AI models, and for reviewers and editors to strictly enforce reproducibility guidelines. A dearth of detailed methods or inaccessible source code and models can raise questions about the authenticity of certain AI innovations, potentially impeding their scientific value and their adoption. Although our sample size inhibits broad generalization, it nonetheless offers key insights on enhancing AI research reproducibility

The Semantic Music Player: A Smart Mobile Player Based on Ontological Structures and Analytical Feature Metadata

Presented at the 2nd Web Audio Conference (WAC), April 4-6, 2016, Atlanta, Georgia.The Semantic Music Player is a cross-platform web and mobile app built with Ionic and the Web Audio API that explores new ways of playing back music on mobile devices, particularly indeterministic, context-dependent, and interactive ways. It is based on Dynamic Music Objects, a format that represents musical content and structure in an abstract way and makes it modifiable within definable constraints. For each Dynamic Music Object, the Semantic Music Player generates a custom graphical interface and enables appropriate user interface controls and mobile sensors based on its requirements. When the object is played back, the player takes spontaneous decisions based on the given structural information and the analytical data and reacts to sensor and user interface inputs. In this paper, we introduce the player and its underlying concepts and give some examples of the potentially infinite amount of use cases and musical results

Industrial Production Process Improvement by a Process Engine Visual Analytics Dashboard

Digitalization reshapes production in a sense that production processes are required to be more flexible and more interconnected to produce products in smaller lot sizes. This makes the process improvement much more challenging, as traditional approaches, which are based on the learning curve, are difficult to apply. Data-driven technologies promise help in learning faster by making use of the massive data volumes collected in production environments. Visual analytics approaches are particularly promising in this regard as they aim to enable engineers with their rich domain knowledge to identify opportunities for process improvements. Based on the assumption that process improvement should be connected with the process engine managing the process execution, we propose a visual analytics dashboard which integrates process models. Based on a case study in the smart factory of Vienna, we conducted two pair analytics sessions. The first results seem promising, whereas domain experts articulate their wish for improvements and future work

Exploring Musical Expression on the Web: Deforming, Exaggerating, and Blending Decomposed Recordings

We introduce a prototype of an educational web application for comparative performance analysis based on source separation and object-based audio techniques. The underlying system decomposes recordings of classical music performances into note events using score-informed source separation and represents the decomposed material using semantic web technologies. In a visual and interactive way, users can explore individual performances by highlighting specific musical aspects directly within the audio and by altering the temporal characteristics to obtain versions in which the micro-timing is exaggerated or suppressed. Multiple performances of the same work can be compared by juxtaposing and blending between the corresponding recordings. Finally, by adjusting the timing of events, users can generate intermediates of multiple performances to investigate their commonalities and di↵erences

A User-Adaptive Automated DJ Web App with Object-Based Audio and Crowd-Sourced Decision Trees

We describe the concepts behind a web-based minimal-UI DJ system that adapts to the user’s preference via sim- ple interactive decisions and feedback on taste. Starting from a preset decision tree modeled on common DJ prac- tice, the system can gradually learn a more customised and user-specific tree. At the core of the system are structural representations of the musical content based on semantic au- dio technologies and inferred from features extracted from the audio directly in the browser. These representations are gradually combined into a representation of the mix which could then be saved and shared with other users. We show how different types of transitions can be modeled using sim- ple musical constraints. Potential applications of the system include crowd-sourced data collection, both on temporally aligned playlisting and musical preference