12,970 research outputs found
The spatiotemporal representation of dance and music gestures using topological gesture analysis (TGA)
SPATIOTEMPORAL GESTURES IN MUSIC AND DANCE HAVE been approached using both qualitative and quantitative research methods. Applying quantitative methods has offered new perspectives but imposed several constraints such as artificial metric systems, weak links with qualitative information, and incomplete accounts of variability. In this study, we tackle these problems using concepts from topology to analyze gestural relationships in space. The Topological Gesture Analysis (TGA) relies on the projection of musical cues onto gesture trajectories, which generates point clouds in a three-dimensional space. Point clouds can be interpreted as topologies equipped with musical qualities, which gives us an idea about the relationships between gesture, space, and music. Using this method, we investigate the relationships between musical meter, dance style, and expertise in two popular dances (samba and Charleston). The results show how musical meter is encoded in the dancer's space and how relevant information about styles and expertise can be revealed by means of simple topological relationships
Dynamic Cues for Network Music Interactions
This paper provides an overview of a cueing system, the Master Cue Generator (MCG) used to trigger performers (humans or computers) over an IP-based network. The performers are scattered in several locations and receive cues to help them interact musically over the net- work. The paper proposes a classification of cues that dynamically evolve and reshape as the performance takes place. This begets the explo- ration of various issues such as how to represent and port a hierarchy of control over a net- worked music performance (NMP) and also takes into account parameters inherent to a net- work such as latency and distance. This ap- proach is based on several years of practice-led research in the field of NMP, a discipline that is gaining grounds within the music technology community both as a practice and through the development of tools and strategies for interact- ing over disparate locations
Computational Topology Techniques for Characterizing Time-Series Data
Topological data analysis (TDA), while abstract, allows a characterization of
time-series data obtained from nonlinear and complex dynamical systems. Though
it is surprising that such an abstract measure of structure - counting pieces
and holes - could be useful for real-world data, TDA lets us compare different
systems, and even do membership testing or change-point detection. However, TDA
is computationally expensive and involves a number of free parameters. This
complexity can be obviated by coarse-graining, using a construct called the
witness complex. The parametric dependence gives rise to the concept of
persistent homology: how shape changes with scale. Its results allow us to
distinguish time-series data from different systems - e.g., the same note
played on different musical instruments.Comment: 12 pages, 6 Figures, 1 Table, The Sixteenth International Symposium
on Intelligent Data Analysis (IDA 2017
Syntonets: Toward A Harmony-Inspired General Model of Complex Networks
We report an approach to obtaining complex networks with diverse topology,
here called syntonets, taking into account the consonances and dissonances
between notes as defined by scale temperaments. Though the fundamental
frequency is usually considered, in real-world sounds several additional
frequencies (partials) accompany the respective fundamental, influencing both
timber and consonance between simultaneous notes. We use a method based on
Helmholtz's consonance approach to quantify the consonances and dissonances
between each of the pairs of notes in a given temperament. We adopt two
distinct partials structures: (i) harmonic; and (ii) shifted, obtained by
taking the harmonic components to a given power , which is henceforth
called the anharmonicity index. The latter type of sounds is more realistic in
the sense that they reflect non-linearities implied by real-world instruments.
When these consonances/dissonances are estimated along several octaves,
respective syntonets can be obtained, in which nodes and weighted edge
represent notes, and consonance/dissonance, respectively. The obtained results
are organized into two main groups, those related to network science and
musical theory. Regarding the former group, we have that the syntonets can
provide, for varying values of , a wide range of topologies spanning the
space comprised between traditional models. Indeed, it is suggested here that
syntony may provide a kind of universal complex network model. The musical
interpretations of the results include the confirmation of the more regular
consonance pattern of the equal temperament, obtained at the expense of a wider
range of consonances such as that in the meantone temperament. We also have
that scales derived for shifted partials tend to have a wider range of
consonances/dissonances, depending on the temperament and anharmonicity
strength
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
Synthesis of variable dancing styles based on a compact spatiotemporal representation of dance
Dance as a complex expressive form of motion is able to convey emotion, meaning and social idiosyncrasies that opens channels for non-verbal communication, and promotes rich cross-modal interactions with music and the environment. As such, realistic dancing characters may incorporate crossmodal information and variability of the dance forms through compact representations that may describe the movement structure in terms of its spatial and temporal organization. In this paper, we propose a novel method for synthesizing beatsynchronous dancing motions based on a compact topological model of dance styles, previously captured with a motion capture system. The model was based on the Topological Gesture Analysis (TGA) which conveys a discrete three-dimensional point-cloud representation of the dance, by describing the spatiotemporal variability of its gestural trajectories into uniform spherical distributions, according to classes of the musical meter. The methodology for synthesizing the modeled dance traces back the topological representations, constrained with definable metrical and spatial parameters, into complete dance instances whose variability is controlled by stochastic processes that considers both TGA distributions and the kinematic constraints of the body morphology. In order to assess the relevance and flexibility of each parameter into feasibly reproducing the style of the captured dance, we correlated both captured and synthesized trajectories of samba dancing sequences in relation to the level of compression of the used model, and report on a subjective evaluation over a set of six tests. The achieved results validated our approach, suggesting that a periodic dancing style, and its musical synchrony, can be feasibly reproduced from a suitably parametrized discrete spatiotemporal representation of the gestural motion trajectories, with a notable degree of compression
Hierarchical Real Time Interapplication Communications
International audienceReal time interapplication communications are a key feature in musical multi-task operating systems. Independent applications can therefore be connected and collaborate by exchanging messages and data through communication channels. All these collaborating applications define a virtual network the user can dynamically configurate. The topology of such virtual network specifies the way applications can be connected together. This paper introduces a new hierarchical topology we recently implemented in our MidiShare multi-task operating system. This approach offers several advantages and particularly when a large number of applications are involved or in a multi-user context
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