LEARNING AND VISUALIZING MUSIC SPECIFICATIONS USING PATTERN GRAPHS

ABSTRACT We describe a system to learn and visualize specifications from song(s) in symbolic and audio formats. The core of our approach is based on a software engineering procedure called specification mining. Our procedure extracts patterns from feature vectors and uses them to build pattern graphs. The feature vectors are created by segmenting song(s) and extracting time and and frequency domain features from them, such as chromagrams, chord degree and interval classification. The pattern graphs built on these feature vectors provide the likelihood of a pattern between nodes, as well as start and ending nodes. The pattern graphs learned from a song(s) describe formal specifications that can be used for human interpretable quantitatively and qualitatively song comparison or to perform supervisory control in machine improvisation. We offer results in song summarization, song and style validation and machine improvisation with formal specifications

Guided Improvisation as Dynamic Calls to an Offline Model

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AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía

Control Improvisation

We formalize and analyze a new automata-theoretic problem termed control improvisation. Given an automaton, the problem is to produce an improviser, a probabilistic algorithm that randomly generates words in its language, subject to two additional constraints: the satisfaction of an admissibility predicate, and the exhibition of a specified amount of randomness. Control improvisation has multiple applications, including, for example, generating musical improvisations that satisfy rhythmic and melodic constraints, where admissibility is determined by some bounded divergence from a reference melody. We analyze the complexity of the control improvisation problem, giving cases where it is efficiently solvable and cases where it is #P-hard or undecidable. We also show how symbolic techniques based on Boolean satisfiability (SAT) solvers can be used to approximately solve some of the intractable cases

‘You should try lying more’: the nomadic impermanence of sound and text in the work of Bill Drummond

Bill Drummond’s work straddles the worlds of popular music, literature and art. Through his books, music and artistic interventions Drummond has engaged with the (im)permanence of culture while manifesting a network of creative associations that give shape to a nebulous series of artistic efforts in a variety of media. His latest project, The17 and its associated book of the same name, explores the impermanence of musical expression, a theme manifested by his deletion of the KLF back catalogue in 1992 and his burning of £1 million pounds in 1994. Yet the concentration on impermanence in Drummond’s musical work is balanced by the possible permanence of language, manifest both in his books and leaflets, as well as in his artworks which are highly logo centric, whether they be graffiti or the painted scores for the 17 project. This article explores Drummond’s work through the Deleuzian filter of nomadism to interrogate the tensions between that which is now and that which has the possibility to always be. Drummond stands in many ways as an anti-theorist, engaging with music, literature and art in nomadic ways that are not always intended by him, providing a network of connections that might seek to evade the very conception of the network itself

Concurrent constraints models of music interaction

International audienceIn this chapter we follow this "economy of means" way to present several vari- eties of CCP calculi, starting from a very basic one and building from it by adding new features. A fundamental one for music applications is the ability to represent temporal behavior. This can be introduced within the context of determinate (tcc, utcc) or non-determinate (ntcc) computation. For the determinate case, we show how the addition of a process abstraction feature (utcc) allows to model dynamic musical structures in a very simple way. In particular, we model a dynamic version of interactive scores ([ALL 07]). For the nondeterminate case, we use the possibility of defining many alternative computational paths to model an agent following different rhythmic patterns constructed from a given basic one. We then go on to consider a more "metrical" notion of time (rtcc) based on uniform ticks used by processes to define their time of execution in a more fine-grained way, or to cause preemption of other processes at more precisely defined points in time. We use these new "real-time" features to describe a simple model of a basic form of musical dissonances

Risk-aware motion planning for automated vehicle among human-driven cars

We consider the maneuver planning problem for automated vehicles when they share the road with human-driven cars and interact with each other using a finite set of maneuvers. Each maneuver is calculated considering input constraints, actuator disturbances and sensor noise, so that we can use a maneuver automaton to perform higher-level planning that is robust against lower-level effects. In order to model the behavior of human-driven cars in response to the intent of the automated vehicle, we use control improvisation to build a probabilistic model. To accommodate for potential mismatches between the learned human model and human driving behaviors, we use a conditional value-at-risk objective function to obtain the optimal policy for the automated vehicle. We demonstrate through simulations that our motion planning framework consisting of an interactive human driving model and risk-aware motion planning strategy makes it possible to adapt to different traffic conditions and confidence levels