Using discovered, polyphonic patterns to filter computer-generated music

A metric for evaluating the creativity of a music-generating system is presented, the objective being to generate mazurka-style music that inherits salient patterns from an original excerpt by Frédéric Chopin. The metric acts as a filter within our overall system, causing rejection of generated passages that do not inherit salient patterns, until a generated passage survives. Over fifty iterations, the mean number of generations required until survival was 12.7, with standard deviation 13.2. In the interests of clarity and replicability, the system is described with reference to specific excerpts of music. Four concepts–Markov modelling for generation, pattern discovery, pattern quantification, and statistical testing–are presented quite distinctly, so that the reader might adopt (or ignore) each concept as they wish

Interactive intelligence: behaviour-based AI, musical HCI and the Turing Test

The field of behaviour-based artificial intelligence (AI), with its roots in the robotics research of Rodney Brooks, is not predominantly tied to linguistic interaction in the sense of the classic Turing test (or, "imitation game"). Yet, it is worth noting, both are centred on a behavioural model of intelligence. Similarly, there is no intrinsic connection between musical AI and the language-based Turing test, though there have been many attempts to forge connections between them. Nonetheless, there are aspects of musical AI and the Turing test that can be considered in the context of non-language-based interactive environments–-in particular, when dealing with real-time musical AI, especially interactive improvisation software. This paper draws out the threads of intentional agency and human indistinguishability from Turing’s original 1950 characterisation of AI. On the basis of this distinction, it considers different approaches to musical AI. In doing so, it highlights possibilities for non-hierarchical interplay between human and computer agents

Music Generation by Deep Learning - Challenges and Directions

In addition to traditional tasks such as prediction, classification and translation, deep learning is receiving growing attention as an approach for music generation, as witnessed by recent research groups such as Magenta at Google and CTRL (Creator Technology Research Lab) at Spotify. The motivation is in using the capacity of deep learning architectures and training techniques to automatically learn musical styles from arbitrary musical corpora and then to generate samples from the estimated distribution. However, a direct application of deep learning to generate content rapidly reaches limits as the generated content tends to mimic the training set without exhibiting true creativity. Moreover, deep learning architectures do not offer direct ways for controlling generation (e.g., imposing some tonality or other arbitrary constraints). Furthermore, deep learning architectures alone are autistic automata which generate music autonomously without human user interaction, far from the objective of interactively assisting musicians to compose and refine music. Issues such as: control, structure, creativity and interactivity are the focus of our analysis. In this paper, we select some limitations of a direct application of deep learning to music generation, analyze why the issues are not fulfilled and how to address them by possible approaches. Various examples of recent systems are cited as examples of promising directions.Comment: 17 pages. arXiv admin note: substantial text overlap with arXiv:1709.01620. Accepted for publication in Special Issue on Deep learning for music and audio, Neural Computing & Applications, Springer Nature, 201

Creativity in Art Music Composition

This thesis investigates what it means to call art music composition creative. Research into the concept of creativity has taken place mostly in science-based disciplines and is reviewed for its relevance. Discussions on what may constitute the foundations of creativity in music are conducted. Musical creativity is not bounded by normativity, consistency, truth-boundedness, optimization or effability for its recognition and is largely aesthetic. Current research methods are mainly explanatory, objective and analytic, and necessarily fall short in understanding musical creativity. It thereby undermines the validity of these methods when used to justify one’s understanding. The undermining invariably takes place by disrupting logical and reasonable expectations. The significance of this research is that it attempts to find and describe essences of the subject matter, the effect of which actually disrupts grounds for finding essences in the first place. It no longer seeks to explain creativity in musical composition. This thesis argues that creativity in art music composition is better understood through philosophical phenomenology than through analysis, where evidence as experience and description naturally includes aesthetic considerations. What composers say is made potentially helpful to understand their musical creativity. They are approached using an interview technique where problem solving, truth-boundedness, optimization and reasonable causality are set aside as essential precepts. Responses are interpreted intuitively to reveal essences present. Trains of thought that reveal essential properties in interview content are intuited. They show that communication is a prominent essence to motivation for being creative. Perceptual attitudes and experiences are often provoked by disruption to sonic expectation. Creativity in art music composition then becomes a generic initial step in the way it communicates and inspires through playing with musical expectation

Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction

The analysis of sequences is important for extracting in- formation from music owing to its fundamentally temporal nature. In this paper, we present a distributed model based on the Restricted Boltzmann Machine (RBM) for learning melodic sequences. The model is similar to a previous suc- cessful neural network model for natural language [2]. It is first trained to predict the next pitch in a given pitch se- quence, and then extended to also make use of information in sequences of note-durations in monophonic melodies on the same task. In doing so, we also propose an efficient way of representing this additional information that takes advantage of the RBM’s structure. Results show that this RBM-based prediction model performs better than previ- ously evaluated n-gram models and also outperforms them in certain cases. It is able to make use of information present in longer sequences more effectively than n-gram models, while scaling linearly in the number of free pa- rameters required