955 research outputs found
Generation of folk song melodies using Bayes transforms
The paper introduces the `Bayes transform', a mathematical procedure for putting data into a hierarchical representation. Applicable to any type of data, the procedure yields interesting results when applied to sequences. In this case, the representation obtained implicitly models the repetition hierarchy of the source. There are then natural applications to music. Derivation of Bayes transforms can be the means of determining the repetition hierarchy of note sequences (melodies) in an empirical and domain-general way. The paper investigates application of this approach to Folk Song, examining the results that can be obtained by treating such transforms as generative models
The 31-Tone Tuning System of Nicola Vicentino and the Toroidal Tonnetz: An Annotated Bibliography
Nicola Vicentinoâs treatise Lâantica musica ridotta alla moderna prattica (1555), from here on Lâantica musica, argues that contrapuntal practices based on modes derived from the diatonic tetrachord are insufficient to express the variety of emotions possible in vocal text settings, and that composers should be inspired by the other Ancient Greek genera as described by Boethius, the chromatic and enharmonic tetrachords. To employ these alternative genera, Vicentino devised an ingenious system that extended quarter-comma mean tone temperament to a thirty-one-tone system that can be neatly approximated by a division of the octave into thirty-one equal parts
Data-driven, memory-based computational models of human segmentation of musical melody
When listening to a piece of music, listeners often identify distinct sections or segments
within the piece. Music segmentation is recognised as an important process in the abstraction
of musical contents and researchers have attempted to explain how listeners
perceive and identify the boundaries of these segments.The present study seeks the development of a system that is capable of performing
melodic segmentation in an unsupervised way, by learning from non-annotated musical
data. Probabilistic learning methods have been widely used to acquire regularities in
large sets of data, with many successful applications in language and speech processing.
Some of these applications have found their counterparts in music research and have
been used for music prediction and generation, music retrieval or music analysis, but
seldom to model perceptual and cognitive aspects of music listening.We present some preliminary experiments on melodic segmentation, which highlight
the importance of memory and the role of learning in music listening. These experiments
have motivated the development of a computational model for melodic segmentation
based on a probabilistic learning paradigm.The model uses a Mixed-memory Markov Model to estimate sequence probabilities
from pitch and time-based parametric descriptions of melodic data. We follow the assumption
that listeners' perception of feature salience in melodies is strongly related
to expectation. Moreover, we conjecture that outstanding entropy variations of certain
melodic features coincide with segmentation boundaries as indicated by listeners.Model segmentation predictions are compared with results of a listening study on
melodic segmentation carried out with real listeners. Overall results show that changes
in prediction entropy along the pieces exhibit significant correspondence with the listeners'
segmentation boundaries.Although the model relies only on information theoretic principles to make predictions
on the location of segmentation boundaries, it was found that most predicted segments
can be matched with boundaries of groupings usually attributed to Gestalt rules.These results question previous research supporting a separation between learningbased
and innate bottom-up processes of melodic grouping, and suggesting that some
of these latter processes can emerge from acquired regularities in melodic data
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
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
Fuzzy Family Ties: Familial Similarity Between Melodic Contours of Different Cardinalities
All melodies have shape: a pattern of ascents, descents, and plateaus that occur as music moves through time. This shapeâor contourâis one of a melodyâs defining characteristics. Music theorists such as Michael Friedmann (1985), Robert Morris (1987), Elizabeth Marvin (1987), and Ian Quinn (1997) have developed models for analyzing contour, but only a few compare contours with different numbers of notes (cardinalities), and fewer still compare entire families of contours. Since these models do not account for familial relations between different-sized contours, they apply only to a limited musical repertoire, and therefore it seems unlikely that they reflect how listeners perceive melodic shape.
This dissertation introduces a new method for evaluating familial similarities between related contours, even if the contours have different cardinalities. My Familial Contour Membership model extends theories of contour transformation by using fuzzy set theory and probability. I measure a contourâs degree of familial membership by examining the contourâs transformational pathway and calculating the probability that each move in the pathway is shared by other family members. Through the potential of differing alignments along these pathways, I allow for the possibility that pathways may be omitted or inserted within a contour that exhibits familial resemblance, despite its different cardinality.
Integrating variable cardinality into contour similarity relations more adequately accounts for familial relationships between contours, opening up new possibilities for analytical application to a wide variety of repertoires. I examine familial relationships between variants of medieval plainchant, and demonstrate how the sensitivity to familial variation illuminated by fuzzy theoretical models can contribute to our understanding of musical ontology. I explain how melodic shape contributes to motivic development and narrative creation in Brahmsâs âRegenliedâ Op. 59, No. 3, and the related Violin Sonata No. 1, Op. 78. Finally, I explore how melodic shape is perceived within the repetitive context of melodic phasing in Steve Reichâs The Desert Music. Throughout each study, I show that a more flexible attitude toward cardinality can open contour theory to more nuanced judgments of similarity and familial membership, and can provide new and valuable insights into one of musicâs most fundamental elements
A Cognitive Information Theory of Music: A Computational Memetics Approach
This thesis offers an account of music cognition based on information theory and memetics. My research strategy is to split the memetic modelling into four layers: Data, Information, Psychology and Application. Multiple cognitive models are proposed for the Information and Psychology layers, and the MDL best-fit models with published human data are selected. Then, for the Psychology layer only, new experiments are conducted to validate the best-fit models.
In the information chapter, an information-theoretic model of musical memory is proposed, along with two competing models. The proposed model exhibited a better fit with human data than the competing models. Higher-level psychological theories are then built on top of this information layer. In the similarity chapter, I proposed three competing models of musical similarity, and conducted a new experiment to validate the best-fit model. In the fitness chapter, I again proposed three competing models of musical fitness, and conducted a new experiment to validate the best-fit model. In both cases, the correlations with human data are statistically significant.
All in all, my research has shown that the memetic strategy is sound, and the modelling results are encouraging. Implications of this research are discussed
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