This paper discusses the use of statistical models for the problem of musical style imitation. Statistical models are created from extant pieces in a stylistic corpus, and have an objective goal which is to accurately classify new pieces. The process of music generation is equated with the problem of sampling from a statistical model. In principle there is no need to make the classical distinction between analytic and synthetic models of music. This paper presents several methods for sampling from an analytic statistical model, and proposes a new approach that maintains the intra opus pattern repetition within an extant piece. A major component of creativity is the adaptation of extant art works, and this is also an efficient way to sample pieces from complex statistical models.

We consider aspects pertinent to evaluating creativity to be input, output and the process by which the output is achieved. These issues

I summarise and attempt to clarify some concepts presented in and arising from Margaret Boden's (1990) descriptive hierarchy of creativity, by formalising the ideas she proposes.

Our aim in this paper is to clarify the range of motivations that have inspired the development of computer programs for the composition of music. We consider this to be important since different methodologies are appropriate for different motivations and goals. We argue that a widespread failure to specify the motivations and goals involved has lead to a methodological malaise in music related research. A brief consideration of some of the earliest attempts to produce computational systems for the composition of music leads us to identify four activities involving the development of computer programs which compose music each of which is inspired by different practical or theoretical motivations. These activities are algorithmic composition, the design of compositional tools, the computational modelling of musical styles and the computational modelling of music cognition. We consider these four motivations in turn, illustrating the problems that have arisen from failing to distinguish between them. We propose a terminology that clearly differentiates the activities defined by the four motivations and present methodological suggestions for research in each domain. While it is clearly important for researchers to embrace developments in related disciplines, we argue that research in the four domains will continue to stagnate unless the motivations and aims of research projects are clearly stated and appropriate methodologies are adopted for developing and evaluating systems that compose music.

To facilitate work on automated breakbeat cutting it was expedient to establish a general framework promoting better code reusability. This framework is a publicly released collection of SuperCollider classes and help files called the BBCut Library. Whilst notionally for the cutting of breakbeat samples, its remit is much wider, into the use of algorithmic composition techniques to cut up any source audio. The library is based upon a specific hierarchy of phrase/block/cut sufficient to implement a wide variety of cut procedures. Hierarchical information allows cut aware effects which can update parameters in coordination with rhythmic events. The benefits of the library include the interchangeable use of any type of synthesis and source with any cut procedure. This makes it much simpler to write a new cut procedure which is immediately able to cut any target signal.

Following the invention of an automatic breakbeat cutting algorithm in the style of early 90s jungle, further experiments are described. These are namely, the use of more advanced techniques to control choices in the original algorithm, and new algorithms for cutting including methods based on campanology and recursion. Automatic

Live music-making using interactive systems is not completely amenable to traditional HCI evaluation metrics such as taskcompletion rates. In this paper we discuss quantitative and qualitative approaches which provide opportunities to evaluate the music-making interaction, accounting for aspects which cannot be directly measured or expressed numerically, yet which may be important for participants. We present case studies in the application of a qualitative method based on Discourse Analysis, and a quantitative method based on the Turing Test. We compare and contrast these methods with each other, and with other evaluation approaches used in the literature, and discuss factors affecting which evaluation methods are appropriate in a given context. Key words: Music, qualitative, quantitative 1.

While music perception is frequently studied in psychological or cognitive scientific research, composition is given far less attention and, in either case, musical creativity is rarely discussed. In this paper we attempt to address this imbalance. Our overall goal is to arrive at a clearer understanding of the psychological mechanisms which support creativity in musical composition. We adopt a cognitive scientific approach to attaining this goal and accordingly our theory is derived from psychological studies of human composers at work. Our investigation is pitched at the computational (rather than the algorithmic or implementational) level(s) of description. Five tentative hypotheses are presented which form the basis of a cognitive theory of creativity in musical composition. Each of these hypotheses makes a specific claim about the functional characteristics of the cognitive processes which support creativity in musical composition. They are motivated by previous research in a number of areas (in particular the psychological, musicological and computational literature) which we discuss in detail. Following the cognitive scientific approach, we are engaged in the implementation of the theory as a computational model and the development of a framework for the objective evaluation of the behaviour of the implemented model. We describe how this framework may be used to refute or corroborate the hypotheses.

Abstract. Computational Creativity is the AI subfield in which we study how to build computational models of creative thought in science and the arts. From an engineering perspective, it is desirable to have concrete measures for assessing the progress made from one version of a program to another, or for comparing and contrasting different software systems for the same creative task. We describe the Turing Test and versions of it which have been used in order to measure progress in Computational Creativity. We show that the versions proposed thus far lack the important aspect of interaction, without which much of the power of the Turing Test is lost. We argue that the Turing Test is largely inappropriate for the purposes of evaluation in Computational Creativity, since it attempts to homogenise creativity into a single (human) style, does not take into account the importance of background and contextual information for a creative act, encourages superficial, uninteresting advances in front-ends, and

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