Limitations from Assumptions in Generative Music Evaluation

The merit of a given piece of music is difficult to evaluate objectively; the merit of a computational system that creates such a piece of music may be even more so. In this article, we propose that there may be limitations resulting from assumptions made in the evaluation of autonomous compositional or creative systems. The article offers a review of computational creativity, evolutionary compositional methods and current methods of evaluating creativity. We propose that there are potential limitations in the discussion and evaluation of generative systems from two standpoints. First, many systems only consider evaluating the final artefact produced by the system whereas computational creativity is defined as a behaviour exhibited by a system. Second, artefacts tend to be evaluated according to recognised human standards. We propose that while this may be a natural assumption, this focus on human-like or human-based preferences could be limiting the potential and generality of future music generating or creative-AI systems

Graph based representation of the music symbolic level. A music information retrieval application

In this work, a new music symbolic level representation system is described. It has been tested in two information retrieval tasks concerning similarity between segments of music and genre detection of a given segment. It could include both harmonic and contrapuntal informations. Moreover, a new large dataset consisting of more than 5000 leadsheets is presented, with meta informations taken from different web databases, including author information, year of first performance, lyrics, genre, etc.ope

MUSIC-MAS: Modelling a Harmonic Composition System with Virtual Organizations to Assist Novice Composers

Many music students today experience difficulties in composing melodies without a prior harmonical guide. While harmony can be helpful in creating a melody the generation of harmony is challenging due to the many factors that must be taken into account, such as style, harmonic functions, musical consonance or aesthetics. Although various solutions have been proposed in the past, our study employs a different expert solution based on virtual organizations to make musical harmonies, which can assist novice improvisers and/or composers. The virtual organizations are implemented with Multi-Agent System (MAS) using PANGEA (Platform for Automatic coNstruction of orGanizations of intElligent Agents), a platform to develop different multiagent systems. The main goal is to simulate an expert multiagent system that can compose harmony following specific rules. To do so, the Harmony Search Algorithm is implemented as the main behavior of the composer agent, and adapted to a Belief-Desire-Intention architecture. The application of a VO has not been previously used in the development of this kind of expert system in music. We measured the quality of the music obtained, by minimizing a mathematical function. Additionally, we developed an evaluation test that positively validates the musical results from the perspective of consonance and usefulness of the composers

Scientific Portal Personalization

Práce se zabývá personalizací webových aplikací a možnostmi jejího využití pro vědecké webové portály. Teoretická část práce seznamuje s principy a metodami personalizace. V rámci praktické části je popsána výsledná aplikace, která na základě vytvořeného uživatelského profilu nabízí služby, jako jsou personalizované vyhledávání, doporučování obsahu a ideální průchod konferencí s paralelními sekcemi.This paper describes personalization of web applications and options of usage for scientific web portals. Theoretical part introduces main personalization principles and methods. Practical part of this paper focuses on resulting application. The application provides personalized services based on created user profile such as personalized search, content recommendation and conference planner.

User-centric Query Refinement and Processing Using Granularity Based Strategies

Under the context of large-scale scientific literatures, this paper provides a user-centric approach for refining and processing incomplete or vague query based on cognitive- and granularity-based strategies. From the viewpoints of user interests retention and granular information processing, we examine various strategies for user-centric unification of search and reasoning. Inspired by the basic level for human problem-solving in cognitive science, we refine a query based on retained user interests. We bring the multi-level, multi-perspective strategies from human problem-solving to large-scale search and reasoning. The power/exponential law-based interests retention modeling, network statistics-based data selection, and ontology-supervised hierarchical reasoning are developed to implement these strategies. As an illustration, we investigate some case studies based on a large-scale scientific literature dataset, DBLP. The experimental results show that the proposed strategies are potentially effective. © 2010 Springer-Verlag London Limited

Functional Scaffolding for Musical Composition: A New Approach in Computer-Assisted Music Composition

While it is important for systems intended to enhance musical creativity to define and explore musical ideas conceived by individual users, many limit musical freedom by focusing on maintaining musical structure, thereby impeding the user\u27s freedom to explore his or her individual style. This dissertation presents a comprehensive body of work that introduces a new musical representation that allows users to explore a space of musical rules that are created from their own melodies. This representation, called functional scaffolding for musical composition (FSMC), exploits a simple yet powerful property of multipart compositions: The pattern of notes and rhythms in different instrumental parts of the same song are functionally related. That is, in principle, one part can be expressed as a function of another. Music in FSMC is represented accordingly as a functional relationship between an existing human composition, or scaffold, and an additional generated voice. This relationship is encoded by a type of artificial neural network called a compositional pattern producing network (CPPN). A human user without any musical expertise can then explore how these additional generated voices should relate to the scaffold through an interactive evolutionary process akin to animal breeding. The utility of this insight is validated by two implementations of FSMC called NEAT Drummer and MaestroGenesis, that respectively help users tailor drum patterns and complete multipart arrangements from as little as a single original monophonic track. The five major contributions of this work address the overarching hypothesis in this dissertation that functional relationships alone, rather than specialized music theory, are sufficient for generating plausible additional voices. First, to validate FSMC and determine whether plausible generated voices result from the human-composed scaffold or intrinsic properties of the CPPN, drum patterns are created with NEAT Drummer to accompany several different polyphonic pieces. Extending the FSMC approach to generate pitched voices, the second contribution reinforces the importance of functional transformations through quality assessments that indicate that some partially FSMC-generated pieces are indistinguishable from those that are fully human. While the third contribution focuses on constructing and exploring a space of plausible voices with MaestroGenesis, the fourth presents results from a two-year study where students discuss their creative experience with the program. Finally, the fifth contribution is a plugin for MaestroGenesis called MaestroGenesis Voice (MG-V) that provides users a more natural way to incorporate MaestroGenesis in their creative endeavors by allowing scaffold creation through the human voice. Together, the chapters in this dissertation constitute a comprehensive approach to assisted music generation, enabling creativity without the need for musical expertise

Sociedades Humano-Agente: Un Caso de Estudio en Creatividad Musical

[ES] Hoy en día, el interés por la creatividad computacional va en aumento en la comunidad científica. Aunque este interés es reciente, hay una gran cantidad de algoritmos, esquemas y procedimientos para desarrollar una máquina tan inteligente, capaz de crear nuevas ideas o nuevas composiciones artísticas

Etno-music: plataforma para la generación colaborativa basada en el análisis musical de la música popular modal en Castilla y León

[ES]El proceso de generación musical con inteligencia artificial resulta de un gran interés desde el punto de vista social, debido a la subjetividad de los resultados musicales, y al dinamismo del contexto. Esta tesis plantea la generación y el análisis musical de una parte del repertorio de la canción popular española. Se realizará una búsqueda, clasificación y análisis de diferentes fuentes sobre música española de tradición oral, en los que la etnomusicología se hace esencial para la consecución de los objetivos. Con los materiales recogidos, se generará una plataforma que permitirá, por un lado, la recolección ordenada y etiquetada del material obtenido y, por otro, aplicar técnicas informáticas para la extracción y análisis de conocimiento. A partir de este contenido, se diseñará una plataforma de colaboración entre hombre y máquina, y de aprendizaje para aquellos interesados en la generación de música popular de tipo melódico en diferentes modos

The construction and evaluation of statistical models of melodic structure in music perception and composition

The prevalent approach to developing cognitive models of music perception and composition is to construct systems of symbolic rules and constraints on the basis of extensive music-theoretic and music-analytic knowledge. The thesis proposed in this dissertation is that statistical models which acquire knowledge through the induction of regularities in corpora of existing music can, if examined with appropriate methodologies, provide significant insights into the cognitive processing involved in music perception and composition. This claim is examined in three stages. First, a number of statistical modelling techniques drawn from the fields of data compression, statistical language modelling and machine learning are subjected to empirical evaluation in the context of sequential prediction of pitch structure in unseen melodies. This investigation results in a collection of modelling strategies which together yield significant performance improvements over existing methods. In the second stage, these statistical systems are used to examine observed patterns of expectation collected in previous psychological research on melody perception. In contrast to previous accounts of this data, the results demonstrate that these patterns of expectation can be accounted for in terms of the induction of statistical regularities acquired through exposure to music. In the final stage of the present research, the statistical systems developed in the first stage are used to examine the intrinsic computational demands of the task of composing a stylistically successful melody The results suggest that the systems lack the degree of expressive power needed to consistently meet the demands of the task. In contrast to previous research, however, the methodological framework developed for the evaluation of computational models of composition enables a detailed empirical examination and comparison of such models which facilitates the identification and resolution of their weaknesses