Perspectives and approaches to determine measures of similarity for musical performances using data analysis algorithms

La caracterización automática de obras musicales, e intérpretes de las mismas, es objeto de investigación en la actualidad. Debido principalmente a la importancia del tópico, pero también al auge tecnológico y la disposición de herramientas computacionales capaces de detectar voz y sonido. El reconocimiento automático de una composición, así como su intérprete y características relevantes desde el punto de vista musical, ha mantenido a una comunidad de investigadores en la búsqueda de medidas que permitan hacer comparaciones e inferir con precisión sobre las características de la composición y el intérprete; sin embargo, esta medida aún está por descubrirse, aunque se han generado diversas técnicas estadístico-computacionales que merecen ser evaluadas y quizás combinadas para fortalecer cualquier investigación en este tópico. Este trabajo, producto de una basta revisión bibliográfica, recoge las principales técnicas y herramientas que han sido utilizadas y propuestas por investigadores en las últimas dos décadas. El documento será de ayuda a los investigadores que decidan emprender estudios, evaluaciones e implementaciones de estas herramientas, así como también aquellos que deseen trabajar en el reconocimiento automático de obras musicales, sus características e intérpretes, o recuperación de información musical.The automatic characterization of a musical composition, and its interpretation, is a current line of research, due to its importance and the technological resources available and computational tools capable for detecting voice and sound. The recognition of an interpreter when listening a composition is simple for a human, but not as simple for machines, thus, this topic has kept a community of researchers in search of measures to compare and accurately recognize the composition characteristics and the interpreter. However, still a general measure has yet to be discovered, although various statistical-computational techniques have been generated and deserve to be evaluated and perhaps combined to strengthen any research on this topic. This work is the product of a comprehensive literature review that collects the main techniques and tools that have been used and proposed by researchers over the last two decades. The document will be of help to researchers who decide to undertake studies, evaluations and implementations of these tools, as well as those who wish to work on automatic recognition of interpreters and characteristics of musical compositions, or music information retrieval

Computational Models of Expressive Music Performance: A Comprehensive and Critical Review

Expressive performance is an indispensable part of music making. When playing a piece, expert performers shape various parameters (tempo, timing, dynamics, intonation, articulation, etc.) in ways that are not prescribed by the notated score, in this way producing an expressive rendition that brings out dramatic, affective, and emotional qualities that may engage and affect the listeners. Given the central importance of this skill for many kinds of music, expressive performance has become an important research topic for disciplines like musicology, music psychology, etc. This paper focuses on a specific thread of research: work on computational music performance models. Computational models are attempts at codifying hypotheses about expressive performance in terms of mathematical formulas or computer programs, so that they can be evaluated in systematic and quantitative ways. Such models can serve at least two purposes: they permit us to systematically study certain hypotheses regarding performance; and they can be used as tools to generate automated or semi-automated performances, in artistic or educational contexts. The present article presents an up-to-date overview of the state of the art in this domain. We explore recent trends in the field, such as a strong focus on data-driven (machine learning) approaches; a growing interest in interactive expressive systems, such as conductor simulators and automatic accompaniment systems; and an increased interest in exploring cognitively plausible features and models. We provide an in-depth discussion of several important design choices in such computer models, and discuss a crucial (and still largely unsolved) problem that is hindering systematic progress: the question of how to evaluate such models in scientifically and musically meaningful ways. From all this, we finally derive some research directions that should be pursued with priority, in order to advance the field and our understanding of expressive music performance