3 research outputs found
Evaluation of Musical Creativity and Musical Metacreation Systems
The field of computational creativity, including musical metacreation, strives to develop artificial systems that are capable of demonstrating creative behavior or producing creative artefacts. But the claim of creativity is often assessed, subjectively only on the part of the researcher and not objectively at all. This article provides theoretical motivation for more systematic evaluation of musical metacreation and computationally creative systems and presents an overview of current methods used to assess human and machine creativity that may be adapted for this purpose. In order to highlight the need for a varied set of evaluation tools, a distinction is drawn among three types of creative systems: those that are purely generative, those that contain internal or external feedback, and those that are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and techniques are suggested to help researchers (1) evaluate their systems' creative process and generated artefacts, and test their impact on the perceptual, cognitive, and affective states of the audience, and (2) build mechanisms for reflection into the creative system, including models of human perception and cognition, to endow creative systems with internal evaluative mechanisms to drive self-reflective processes. The first type of evaluation can be considered external to the creative system and may be employed by the researcher to both better understand the efficacy of their system and its impact and to incorporate feedback into the system. Here we take the stance that understanding human creativity can lend insight to computational approaches, and knowledge of how humans perceive creative systems and their output can be incorporated into artificial agents as feedback to provide a sense of how a creation will impact the audience. The second type centers around internal evaluation, in which the system is able to reason about its own behavior and generated output. We argue that creative behavior cannot occur without feedback and reflection by the creative/metacreative system itself. More rigorous empirical testing will allow computational and metacreative systems to become more creative by definition and can be used to demonstrate the impact and novelty of particular approaches
The Perception of Accents in Pop Music Melodies
We examine several theoretical and empirical approaches to melodic accent perception and propose a heuristic classification system of formalized accent rules. To evaluate the validity of the accent rules a listening experiment was carried out. 29 participants had to rate every note of 15 pop music melodies presented as audio excerpts and as monophonic MIDI renditions for their perceived accent strength on a rating scale. The ratings were compared to accent predictions from 38 formalized, mainly binary accent rules. Two statistical procedures (logistic regression, and regression trees) were subsequently used in a data mining approach to determine a model consisting of an optimally weighted combination of smaller rule subset to predict the accents votes of the participants. Model evaluation on a set of unseen melodies indicates a very good predictive performance of both statistical models for the participants' votes obtained for the MIDI renditions. The two models derived for the audio data perform less well but still at an acceptable level. An analysis of the model components shows that Gestalt rules covering several different aspects of a monophonic melody are of importance for human accent perception. Among the aspects covered by both models are pitch interval structure, pitch contour, note duration, metrical position, as well as the position of a note within a phrase. In contrast, both audio models incorporate mainly rules relating to metre and syncopations. Potential applications of the presented accent models in automatic music analysis as well as options for future research following this computational approach are discussed