The automatic orchestration problem is that of assigning instruments or sounds to the notes of an unorchestrated score. This is related to, but distinct from, problems of automatic expressive interpretation. A simple algorithm is described that successfully orchestrates scores based on analysis of one musical structure -- the "Z-chain."

Xa-lan is a computer program written in Common-LISP to generate music scores with a high level of notational/symbolic expressivity. Generation is driven by audio-analysis of melodic profiles. Once a melodic contour is input to the software, graphic transformations of the original profile stochastically control the different notational elements of the score. The Xa-lan routines display their final output using the ‘Expressive Notation Package’ of PWGL, a LISP-based visual composition environment. A full range of traditional and non-conventional music notation elements can be algorithmically generated with Xa-lan, retrieving to the user a ‘ready-to-play’ or fully ex-pressive music score.

This demonstration presents MaestroGenesis, a program that helps users create complete polyphonic musical pieces from as little as a simple, human composed monophonic melody. MaestroGenesis creates music by exploiting two key ideas behind the functional scaffolding for musical composition (FSMC) approach: (1) that music a function of time and (2) that functional transformations of initial human starting melodies, or scaffolds, inherit some of the essential human qualities contained in the scaffold. Music in FSMC is represented as a functional relationship between the scaffold and a generated accompaniment. The GUI helps users evolve these functions by importing and developing their music through a breeding process akin to animal breeding, called interactive evolutionary computation. Some resulting pieces are indistinguishable from completely human-composed pieces.

In the proposed demo we will present a method for design of musical composition using the Audio Oracle (AO) - a machine improvisation method that constructs and produces variation from a music recording using a graph of repeated factors found in the recording. The compositional / improvisation use of AO involves controlling the rate of recombination, the length of common history (length of the repeated factors) and selection of regions in the AO where the machine operates. One of the challenges in working with AO is understanding the generative potential of different audio materials and marking and allocating regions in a recording where AO should operate to achieve the desired musical result. Recently we introduced a novel analysis method "on top" of the AO structure that captures aspects of order and complexity that we call Information Rate. This measure, belonging broadly to a field of study know as Musical Information Dynamics, is related to Bense formulation of aesthetics in terms of entropy and compression. In the demo we will briefly explain the theory behind Audio Oracle and Information Rate and demonstrate a process of designing a composition / structured improvisation based on analysis of the AO graph. In contrast to the usual live interaction method where both the audio input to the oracle and the improvisation are done "on the fly", in this presentation the audio analysis will be done prior to the presentation, while during the talk we will show the process of planning a composition and then do a live performance with the AO based on this design.

Imagine that a budding composer suffers from writer's block partway through devising a melody. A system called FreshJam is demonstrated, which offers a solution to this problem in the form of an interactive composition assistant; an algorithm that analyzes the notes composed so far, makes a comparison with an indexed corpus of existing music, and suggests a possible next note by choosing randomly among continuations of matched melody fragments. We provide a demonstration of FreshJam as an aid in stylistic composition, and of its potential to be more iterative than existing composition assistants such as PG Music's Band in a Box or Microsoft's Songsmith.

Neural networks and recurrent neural networks have been employed to learn, generalize, and generate musical examples and pieces. Yet, these models typically suffer from an inability to characterize and reproduce the long-term dependencies of musical structure, resulting in products that seem to wander aimlessly. We describe and examine three novel hierarchical models that explicitly operate on multiple structural levels. A three layer model is presented, then a weighting policy is added with two different methods of control attempting to maximize global network learning. While the results do not have sufficient structure beyond the phrase or section level, they do evince autonomous generation of recognizable medium-level structures.

In this paper we introduce a prototype of ’meta-score’, a novel visual editor in PWGL, aimed at defining the structural, temporal and procedural properties of a musical composition. Meta-score is a music notation editor, thus, the score can be created manually by inputting the information using a GUI. However, meta-score extends the concept of a musical score so that the musical content can be defined not only manually but also procedurally. The composition is defined by placing scores (hence the name meta-score) on a timeline, creating dependencies between the objects, and defining the compositional processes associated with them. Meta-score presents the users with a three-stage compositional process beginning from the sketching of the overall structure along with the associated harmonic, rhythmic and melodic material; continuing with the procedural description of the composition and ending with the automatic production of the performance score. In this paper, we describe the present state of meta-score.

The Melody Triangle is an interface for the discovery of melodic materials, where the input – positions within a triangle – directly map to information theoretic properties of the output. A model of human expectation and surprise in the perception of music, information dynamics, is used to ‘map out’ a musical generative system’s parameter space. This enables a user to explore the possibilities afforded by a generative algorithm, in this case Markov chains, not by directly selecting parameters, but by specifying the subjective predictability of the output sequence. We describe some of the relevant ideas from information dynamics and how the Melody Triangle is defined in terms of these. We describe its incarnation as a screen based performance tool and compositional aid for the generation of musical textures; the users control at the abstract level of randomness and predictability, and some pilot studies carried out with it. We also briefly outline a multi-user installation, where collabo- ration in a performative setting provides a playful yet informative way to explore expectation and surprise in music, and a forthcoming mobile phone version of the Melody Triangle.

Quantum mechanical systems exist as superpositions of complementary states that collapse to classical, concrete states upon becoming entangled with the measurement apparatus of observer-participants. A musical composition and its performance constitute a quantum system. Historically, conventional musical notation has presented the appearance of a composition as a deterministic, concrete entity, with interpretation approached as an extrinsic act. This historical perspective inhabits a subspace of the available quantum space. A quantum musical system unifies the composition, instruments, situated performance and perception as a superposition of musical events that collapses to concrete musical events via the interactions and perceptions of performers and audience. A composer captures superposed musical events via implicit or explicit conditional event probabilities, and human and/or machine performers create music by collapsing interrelated probabilities to zeros and ones via observer-participancy.

One of the goals of the study of music theory is to develop sets of rules to describe different styles of music. By formalising these rules so that their semantics are machine intelligible, it is possible to use computers to reason about and analyse these rules -- computational music theory. Anton is an automatic composition system based on this approach. It formalises the rules of Renaissance Counterpoint using AnsProlog and uses an answer set solver to compose pieces. This paper discusses Anton, presenting the ideas behind the system and focusing on the challenges of modelling and synthesising rhythm.