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Making sense of a musical excerpt is an acquired skill that depends on previous musical experience. Having acquired familiarity with different types of chords, a listener can distinguish tones in a musical texture that outline these chords (i.e., chord tones) from ornamental tones such as neighbor or passing notes that elaborate the chord tones. However, music-theoretical definitions of chord types usually only mention chord tones, excluding typical figurations. The aim of this project is to investigate (i) how knowledge about (chord-specific) figurations can be incorporated into characterizations of chord types and (ii) how these characterizations can be acquired by the listener. To this end, we develop a computational model of chord types that distinguishes chord tones and “figuration tones” and can be learned using Bayesian inference following methods in computational cognitive science. This model is trained on two datasets using Bayesian variational inference, comprising scores of Western classical and popular music, respectively, and containing harmonic annotations as well as heuristically determined note-type labels. We find that the proposed characterization of chords is indeed learnable and the specific inferred profiles match previous music-theoretic accounts. In addition, we can observe patterns in the use of figuration, such as the distribution of figuration tones being related to the diatonic contexts in which chords appear and chord types differing in their predisposition to generate non-chord tones. Moreover, the differences in figuration distributions between the two corpora indicate style-specific peculiarities in the role and usage of figurations. The different patterns of typical figuration tones for specific chord types indicate that harmony and figuration are not independent.

The explosion of content generated by artificial intelligence (AI) models has initiated a cultural shift in arts, music, and media, whereby roles are changing, values are shifting, and conventions are challenged. The vast, readily available dataset of the Internet has created an environment for AI models to be trained on any content on the Web. With AI models shared openly and used by many globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring to music, arts, and new media?

This preface briefly presents the organisation and outcomes of the workshop and tutorial days of the Third International Conference on Hybrid Human-Artificial Intelligence (HHAI) 2024, introducing the conference topic and giving key highlights of the specifics of the proceedings.

This article explores the rapidly developing field of Critical AI Studies and its relation to issues of class and capitalism through a hybrid approach based on distant reading of a newly collected corpus of 300 full-text scientific articles, the creation of which is itself a first attempt at properly delineating the field. We find that words related to issues of class are predominantly but not exclusively confined to a set of studies that make up their own distinct subfield of Critical AI Studies, in contrast to, e.g., issues of race and gender, which are more broadly present in the corpus.

Order-preserving DAG grammars (OPDGs) is a formalism for representing languages of structurally restricted graphs. As demonstrated in [17], they are sufficiently expressive to model abstract meaning representations in natural language processing, a graph-based form of semantic representation in which nodes encode objects and edges relations. At the same time, they can be parsed in O (n² + nm) , where m and n are the sizes of the grammar and the input graph, respectively. In this work, we provide an initial algebra semantic for OPDGs, which allows us to view them as regular tree grammars under an equivalence theory. This makes it possible to transfer results from the field of formal tree languages to the domain of OPDGs, both in the unweighted and the weighted case. In particular, we show that deterministic OPDGs can be minimised efficiently, and that they are learnable under the \minimal adequeate teacher" paradigm, that is, by querying an oracle for equivalence between languages, and membership of individual graphs. To conclude, we demonstrate that the languages generated by OPDGs are definable in monadic second-order logic.

In comparison to computational linguistics, with its abundance of natural-language datasets, corpora of music analyses are rather fewer and generally smaller. This is partly due to difficulties inherent to the encoding of music analyses, whose multimodal representations—typically a combination of music notation, graphic notation, and natural language—are designed for communication between human musician-analysts, not for automated large-scale data analysis. Analyses based on hierarchical models of tonal structure, such as Heinrich Schenker’s, present additional notational and encoding challenges, since they establish relations between non- adjacent tones, and typically interpret successions of tones as expressions of abstract chordal sonorities, which may not be literally present in the music score. Building on a published XML format by Rizo and Marsden (2019), which stores analyses alongside symbolically encoded scores, this paper presents a generic graph model for reasoning about music analyses, as well as a graphical web application for creating and encoding music analyses in the aforementioned XML format. Several examples are given showing how various techniques of music analysis, primarily but not necessarily hierarchical, might be unambiguously represented through this model.

It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even under seemingly harsh restrictions. This means that the parsing problem is difficult even in the non-uniform setting, in which the grammar is considered to be fixed rather than being part of the input. Little is known about restrictions under which truly uniform polynomial parsing is possible. In this paper we propose a low-degree polynomial-time algorithm that solves the uniform parsing problem for a restricted type of hyperedge-replacement grammars which we expect to be of interest for practical applications.

Kepler Concordia, a new scientific and musical instrument enabling players to explore the solar system and other data within immersive extended-reality (XR) platforms, is being designed by a diverse team of musicians, artists, scientists and engineers using audio-first principles. The core instrument modules will be launched in 2019 for the 400th anniversary of Johannes Kepler's Harmonies of the World, in which he laid out a framework for the harmony of geometric form as well as the three laws of planetary motion. Kepler's own experimental process can be understood as audio-first because he employed his understanding of Western Classical music theory to investigate and discover the heliocentric, elliptical behaviour of planetary orbits. Indeed, principles of harmonic motion govern much of our physical world and show up at all scales in mathematics and physics. Few physical systems, however, offer such rich harmonic complexity and beauty as our own solar system. Concordia is a musical instrument that is modular, extensible and designed to allow players to generate and explore transparent sonifications of planetary movements rooted in the musical and mathematical concepts of Johannes Kepler as well as researchers who have extended Kepler's work, such as Hartmut Warm. Its primary function is to emphasise the auditory experience by encouraging musical explorations using sonification of geometric and relational information of scientifically accurate planetary ephemeris and astrodynamics. Concordia highlights harmonic relationships of the solar system through interactive sonic immersion. This article explains how we prioritise data sonification and then add visualisations and gamification to create a new type of experience and creative distributed-ledger powered ecosystem. Kepler Concordia facilitates the perception of music while presenting the celestial harmonies through multiple senses, with an emphasis on hearing, so that, as Kepler wrote, 'the mind can seize upon the patterns'.

Grammatical models which represent the hierarchical structure of chord sequences have proven very useful in recent analyses of Jazz harmony. A critical resource for building and evaluating such models is a ground-truth database of syntax trees that encode hierarchical analyses of chord sequences. In this paper, we introduce the Jazz Harmony Treebank (JHT), a dataset of hierarchical analyses of complete Jazz standards. The analyses were created and checked by experts, based on lead sheets from the open iRealPro collection. The JHT is publicly available in JavaScript Object Notation (JSON), a human-understandable and machine-readable format for structured data. We additionally discuss statistical properties of the corpus and present a simple open-source web application for the graphical creation and editing of trees which was developed during the creation of the dataset.

The field of semantic modelling concerns formal models for semantics, that is, formal structures for the computational and algorithmic processing of meaning. This thesis concerns formal graph languages motivated by this field. In particular, we investigate two formalisms: Order-Preserving DAG Grammars (OPDG) and Order-Preserving Hyperedge Replacement Grammars (OPHG), where OPHG generalise OPDG.

Graph parsing is the practise of, given a graph grammar and a graph, to determine if, and in which way, the grammar could have generated the graph. If the grammar is considered fixed, it is the non-uniform graph parsing problem, while if the grammars is considered part of the input, it is named the uniform graph parsing problem. Most graph grammars have parsing problems known to be NP-complete, or even exponential, even in the non-uniform case. We show both OPDG and OPHG to have polynomial uniform parsing problems, under certain assumptions.

We also show these parsing algorithms to be suitable, not just for determining membership in graph languages, but for computing weights of graphs in graph series.

Additionally, OPDG is shown to have several properties common to regular languages, such as MSO definability and MAT learnability. We moreover show a direct corresponcence between OPDG and the regular tree grammars.

Finally, we present some limited practical experiments showing that real-world semantic graphs appear to mostly conform to the requirements set by OPDG, after minimal, reversible processing.