Analysing symbolic music with probabilistic grammars

Recent developments in computational linguistics offer ways to approach the analysis of musical structure by inducing probabilistic models (in the form of grammars) over a corpus of music. These can produce idiomatic sentences from a probabilistic model of the musical language and thus offer explanations of the musical structures they model. This chapter surveys historical and current work in musical analysis using grammars, based on computational linguistic approaches. We outline the theory of probabilistic grammars and illustrate their implementation in Prolog using PRISM. Our experiments on learning the probabilities for simple grammars from pitch sequences in two kinds of symbolic musical corpora are summarized. The results support our claim that probabilistic grammars are a promising framework for computational music analysis, but also indicate that further work is required to establish their superiority over Markov models

Automatic identification of musical schemata

This study was stimulated by the Galant musical schemata theory (GMST), an example–based learning and compositional practice that peaked in popularity around the early 18th century in Europe, suggesting a culturally–defined classification of polyphonic patterns. Under the premises of the GMST and by relating notions from psychology towards a cognitive model for musical schemata identification, an explanatory system based on music-analytical thought–patterns was examined, aiming to describe the mental processes involved in three accumulative operations: a) the schematic analysis of music notation into a stream of salient musical elements and, eventually, GMST–related musical structures, providing the standard form of music notation interpretation for the examined model; b) the example–based learning of musical schemata definitions from annotated examples, and c) the discovery of – similar to the Galant – musical schemata family–types in corpora. The proposed music–analytical model was tested with a novel computational system performing three tasks accordingly: i) search, matching representations of Galant musical schemata prototypes and examining similarity models; ii) classification, classifying segments of schematic analysis according to musical schemata family–type definitions that are extracted and maintained utilising annotated examples and pattern detection methods, and iii) polyphonic pattern extraction, examining methods that form and categorise musical schemata structures. The proposed model was evaluated employing the technological research methodology, and computational experiments quantified the performance of the computational system implementing the aforementioned tasks by utilising Galant musical schemata–annotated datasets and task–oriented performance metrics. Results show a functional cognitive model for complex music–analytical operations with polyphonic patterns, suggesting methodological explanations as to how these may be addressed by the initiate. Based on the foundations established in this project, it may in the future become possible to develop computational tools that have applications in music education and musicological research

Melody

Melody is a fundamental concept in Western musical thought; it connotes the form and affective power of successive sounds in motion, perceived as an aesthetic unity. Yet for many writers, melody does not exist as an autonomous form, and for those who credit its existence, few agree on what it is, or how it functions in relation to harmonic voice leading and phrase rhythm. This chapter examines the historical emergence of a theory of melody in the West, from Aristoxenus to Leonard Bernstein; it traces the rich intellectual currents that saw melody variously coupled to ideas of voice, schemes of rhythmic symmetry, overtones, spatial organization, theories of evolution, and computational analysis

Melody

Melody is a fundamental concept in Western musical thought; it connotes the form and affective power of successive sounds in motion, perceived as an aesthetic unity. Yet for many writers, melody does not exist as an autonomous form, and for those who credit its existence, few agree on what it is, or how it functions in relation to harmonic voice leading and phrase rhythm. This chapter examines the historical emergence of a theory of melody in the West, from Aristoxenus to Leonard Bernstein; it traces the rich intellectual currents that saw melody variously coupled to ideas of voice, schemes of rhythmic symmetry, overtones, spatial organization, theories of evolution, and computational analysis

Methodological considerations concerning manual annotation of musical audio in function of algorithm development

In research on musical audio-mining, annotated music databases are needed which allow the development of computational tools that extract from the musical audiostream the kind of high-level content that users can deal with in Music Information Retrieval (MIR) contexts. The notion of musical content, and therefore the notion of annotation, is ill-defined, however, both in the syntactic and semantic sense. As a consequence, annotation has been approached from a variety of perspectives (but mainly linguistic-symbolic oriented), and a general methodology is lacking. This paper is a step towards the definition of a general framework for manual annotation of musical audio in function of a computational approach to musical audio-mining that is based on algorithms that learn from annotated data. 1

A Standardised Procedure for Evaluating Creative Systems: Computational Creativity Evaluation Based on What it is to be Creative

Computational creativity is a flourishing research area, with a variety of creative systems being produced and developed. Creativity evaluation has not kept pace with system development with an evident lack of systematic evaluation of the creativity of these systems in the literature. This is partially due to difficulties in defining what it means for a computer to be creative; indeed, there is no consensus on this for human creativity, let alone its computational equivalent. This paper proposes a Standardised Procedure for Evaluating Creative Systems (SPECS). SPECS is a three-step process: stating what it means for a particular computational system to be creative, deriving and performing tests based on these statements. To assist this process, the paper offers a collection of key components of creativity, identified empirically from discussions of human and computational creativity. Using this approach, the SPECS methodology is demonstrated through a comparative case study evaluating computational creativity systems that improvise music

Towards a style-specific basis for computational beat tracking

Outlined in this paper are a number of sources of evidence, from psychological, ethnomusicological and engineering grounds, to suggest that current approaches to computational beat tracking are incomplete. It is contended that the degree to which cultural knowledge, that is, the specifics of style and associated learnt representational schema, underlie the human faculty of beat tracking has been severely underestimated. Difficulties in building general beat tracking solutions, which can provide both period and phase locking across a large corpus of styles, are highlighted. It is probable that no universal beat tracking model exists which does not utilise a switching model to recognise style and context prior to application

Topology of Networks in Generalized Musical Spaces

The abstraction of musical structures (notes, melodies, chords, harmonic or rhythmic progressions, etc.) as mathematical objects in a geometrical space is one of the great accomplishments of contemporary music theory. Building on this foundation, I generalize the concept of musical spaces as networks and derive functional principles of compositional design by the direct analysis of the network topology. This approach provides a novel framework for the analysis and quantification of similarity of musical objects and structures, and suggests a way to relate such measures to the human perception of different musical entities. Finally, the analysis of a single work or a corpus of compositions as complex networks provides alternative ways of interpreting the compositional process of a composer by quantifying emergent behaviors with well-established statistical mechanics techniques. Interpreting the latter as probabilistic randomness in the network, I develop novel compositional design frameworks that are central to my own artistic research

Generating Music from Literature

We present a system, TransProse, that automatically generates musical pieces from text. TransProse uses known relations between elements of music such as tempo and scale, and the emotions they evoke. Further, it uses a novel mechanism to determine sequences of notes that capture the emotional activity in the text. The work has applications in information visualization, in creating audio-visual e-books, and in developing music apps