Probabilistic models for melodic prediction

AbstractChord progressions are the building blocks from which tonal music is constructed. The choice of a particular representation for chords has a strong impact on statistical modeling of the dependence between chord symbols and the actual sequences of notes in polyphonic music. Melodic prediction is used in this paper as a benchmark task to evaluate the quality of four chord representations using two probabilistic model architectures derived from Input/Output Hidden Markov Models (IOHMMs). Likelihoods and conditional and unconditional prediction error rates are used as complementary measures of the quality of each of the proposed chord representations. We observe empirically that different chord representations are optimal depending on the chosen evaluation metric. Also, representing chords only by their roots appears to be a good compromise in most of the reported experiments

A Functional Taxonomy of Music Generation Systems

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.Comment: survey, music generation, taxonomy, functional survey, survey, automatic composition, algorithmic compositio

Generation of Two-Voice Imitative Counterpoint from Statistical Models

Generating new music based on rules of counterpoint has been deeply studied in music informatics. In this article, we try to go further, exploring a method for generating new music based on the style of Palestrina, based on combining statistical generation and pattern discovery. A template piece is used for pattern discovery, and the patterns are selected and organized according to a probabilistic distribution, using horizontal viewpoints to describe melodic properties of events. Once the template is covered with patterns, two-voice counterpoint in a florid style is generated into those patterns using a first-order Markov model. The template method solves the problem of coherence and imitation never addressed before in previous research in counterpoint music generation. For constructing the Markov model, vertical slices of pitch and rhythm are compiled over a large corpus of dyads from Palestrina masses. The template enforces different restrictions that filter the possible paths through the generation process. A double backtracking algorithm is implemented to handle cases where no solutions are found at some point within a generation path. Results are evaluated by both information content and listener evaluation, and the paper concludes with a proposed relationship between musical quality and information content. Part of this research has been presented at SMC 2016 in Hamburg, Germany

A Model for Scale-Degree Reinterpretation: Melodic Structure, Modulation, and Cadence Choice in the Chorale Harmonizations of J. S. Bach

This paper reports a corpus study of the 371 chorale harmonizations by J. S. Bach. Specifically, this study investigates what kinds of events are typical at phrase endings given various melodic conditions, i.e., how well melodic structure is a predictor of modulation and cadence choices. Each fermata event was analyzed by ear and encoded with regard to the local key area and the cadence type. The frequency of each cadence type was then tabulated with respect to categorizations of the melodic structure (in terms of the intervallic pattern and scale degree content) prior to the fermata. It is shown that most fermata events can be categorized by a small collection of event types. As a result, a simplified conceptual model of cadence choice is posited. This model proposes that a basic harmonization default is to (re-)interpret the soprano note at the fermata as scale-degree 1, 2, or 3 in some closely-related key area via an authentic or half cadence. The efficacy of this model is found to be very good, especially given certain conditions. Moreover, an overall success rate above 90% can be achieved through only four additional concepts

The song system of the human brain.

Although sophisticated insights have been gained into the neurobiology of singing in songbirds, little comparable knowledge exists for humans, the most complex singers in nature. Human song complexity is evidenced by the capacity to generate both richly structured melodies and coordinated multi-part harmonizations. The present study aimed to elucidate this multi-faceted vocal system by using 15O-water positron emission tomography to scan ?listen and respond? performances of amateur musicians either singing repetitions of novel melodies, singing harmonizations with novel melodies, or vocalizing monotonically. Overall, major blood flow increases were seen in the primary and secondary auditory cortices, primary motor cortex, frontal operculum, supplementary motor area, insula, posterior cerebellum, and basal ganglia. Melody repetition and harmonization produced highly similar patterns of activation. However, whereas all three tasks activated secondary auditory cortex (posterior Brodmann Area 22), only melody repetition and harmonization activated the planum polare (BA 38). This result implies that BA 38 is responsible for an even higher level of musical processing than BA 22. Finally, all three of these ?listen and respond? tasks activated the frontal operculum (Broca's area), a region involved in cognitive/motor sequence production and imitation, thereby implicating it in musical imitation and vocal learning

AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía