Haptics for the development of fundamental rhythm skills, including multi-limb coordination

This chapter considers the use of haptics for learning fundamental rhythm skills, including skills that depend on multi-limb coordination. Different sensory modalities have different strengths and weaknesses for the development of skills related to rhythm. For example, vision has low temporal resolution and performs poorly for tracking rhythms in real-time, whereas hearing is highly accurate. However, in the case of multi-limbed rhythms, neither hearing nor sight are particularly well suited to communicating exactly which limb does what and when, or how the limbs coordinate. By contrast, haptics can work especially well in this area, by applying haptic signals independently to each limb. We review relevant theories, including embodied interaction and biological entrainment. We present a range of applications of the Haptic Bracelets, which are computer-controlled wireless vibrotactile devices, one attached to each wrist and ankle. Haptic pulses are used to guide users in playing rhythmic patterns that require multi-limb coordination. One immediate aim of the system is to support the development of practical rhythm skills and multi-limb coordination. A longer-term goal is to aid the development of a wider range of fundamental rhythm skills including recognising, identifying, memorising, retaining, analysing, reproducing, coordinating, modifying and creating rhythms – particularly multi-stream (i.e. polyphonic) rhythmic sequences. Empirical results are presented. We reflect on related work, and discuss design issues for using haptics to support rhythm skills. Skills of this kind are essential not just to drummers and percussionists but also to keyboards players, and more generally to all musicians who need a firm grasp of rhythm

The rhythmical development of samba between 1910 and 1940: Transformation of emergence? A reevaluation of the Bantu contribution in the form of timelines as a rhythm concept

From a history of persecution and repression in the first decades of the 20th century samba became by 1940, the strongest cultural manifestation to represent Brazilian identity. In the space of a mere three decades, this musical form spread from the lower classes and permeated the highest echelons of Brazilian society. Samba, as it is known today in Brazil, inherits its rhythmical shape from the Samba Batucado. What I suggest is that the rhythmic cell that guided Samba during its rise to the position of Brazilian national music did not appear suddenly at the end of the 1920s with the Estácio composers, it already existed in other musical forms, as a creative concept and the Brazilian national style of music was the result of, not a transformation of the first sambas recorded, but in fact, the emergence into commercial popular music of a rhythmical concept which was part of the culture of the disenfranchised ex-slaves and their immediate descendents. I further suggest that this “timeline” is a direct inheritance from the Bantu culture of the Congo/Angolan slaves brought to Brazil, for it is from those areas in Africa that the majority of slaves in the states of Rio de Janeiro, Sao Paulo and Minas Gerais came from

Feeling the beat where it counts: fostering multi-limb rhythm skills with the haptic drum kit

This paper introduces and explores a tool known as the Haptic Drum Kit. The Haptic Drum Kit employs four computer-controlled vibrotactile devices, one attached to each limb via the wrists and ankles. In the mode of use discussed in this paper, haptic pulses are used to guide the playing, on a drum kit, of rhythmic patterns that require multi-limb co-ordination. The immediate aim is to foster rhythm skills and multi-limb coordination. A broader aim is to systematically develop skills in recognizing, identifying, memorizing, retaining, analyzing, reproducing and composing monophonic and polyphonic rhythms. We consider the implications of three different theories for this approach: the work of the music educator Dalcroze (1865-1950 [1]; the entrainment theory of human rhythm perception and production [2,3]; and sensory motor contingency theory [4]. In this paper we introduce the Haptic Drum Kit; consider the implications of the above theories for this approach; report on a design study; and identify and discuss a variety of emerging design issues. As part of the design study, audio and haptic guidance was compared for five people learning to play polyphonic drum patterns of varying complexity. The results indicate that beginning drummers are able to learn intricate drum patterns from the haptic stimuli alone, although haptic plus audio is the mode of presentation preferred by subjects

Computational methods for percussion music analysis : the afro-uruguayan candombe drumming as a case study

Most of the research conducted on information technologies applied to music has been largely limited to a few mainstream styles of the so-called `Western' music. The resulting tools often do not generalize properly or cannot be easily extended to other music traditions. So, culture-specific approaches have been recently proposed as a way to build richer and more general computational models for music. This thesis work aims at contributing to the computer-aided study of rhythm, with the focus on percussion music and in the search of appropriate solutions from a culture specifc perspective by considering the Afro-Uruguayan candombe drumming as a case study. This is mainly motivated by its challenging rhythmic characteristics, troublesome for most of the existing analysis methods. In this way, it attempts to push ahead the boundaries of current music technologies. The thesis o ers an overview of the historical, social and cultural context in which candombe drumming is embedded, along with a description of the rhythm. One of the specific contributions of the thesis is the creation of annotated datasets of candombe drumming suitable for computational rhythm analysis. Performances were purposely recorded, and received annotations of metrical information, location of onsets, and sections. A dataset of annotated recordings for beat and downbeat tracking was publicly released, and an audio-visual dataset of performances was obtained, which serves both documentary and research purposes. Part of the dissertation focused on the discovery and analysis of rhythmic patterns from audio recordings. A representation in the form of a map of rhythmic patterns based on spectral features was devised. The type of analyses that can be conducted with the proposed methods is illustrated with some experiments. The dissertation also systematically approached (to the best of our knowledge, for the first time) the study and characterization of the micro-rhythmical properties of candombe drumming. The ndings suggest that micro-timing is a structural component of the rhythm, producing a sort of characteristic "swing". The rest of the dissertation was devoted to the automatic inference and tracking of the metric structure from audio recordings. A supervised Bayesian scheme for rhythmic pattern tracking was proposed, of which a software implementation was publicly released. The results give additional evidence of the generalizability of the Bayesian approach to complex rhythms from diferent music traditions. Finally, the downbeat detection task was formulated as a data compression problem. This resulted in a novel method that proved to be e ective for a large part of the dataset and opens up some interesting threads for future research.La mayoría de la investigación realizada en tecnologías de la información aplicadas a la música se ha limitado en gran medida a algunos estilos particulares de la así llamada música `occidental'. Las herramientas resultantes a menudo no generalizan adecuadamente o no se pueden extender fácilmente a otras tradiciones musicales. Por lo tanto, recientemente se han propuesto enfoques culturalmente específicos como forma de construir modelos computacionales más ricos y más generales. Esta tesis tiene como objetivo contribuir al estudio del ritmo asistido por computadora, desde una perspectiva cultural específica, considerando el candombe Afro-Uruguayo como caso de estudio. Esto está motivado principalmente por sus características rítmicas, problemáticas para la mayoría de los métodos de análisis existentes. Así , intenta superar los límites actuales de estas tecnologías. La tesis ofrece una visión general del contexto histórico, social y cultural en el que el candombe está integrado, junto con una descripción de su ritmo. Una de las contribuciones específicas de la tesis es la creación de conjuntos de datos adecuados para el análisis computacional del ritmo. Se llevaron adelante sesiones de grabación y se generaron anotaciones de información métrica, ubicación de eventos y secciones. Se disponibilizó públicamente un conjunto de grabaciones anotadas para el seguimiento de pulso e inicio de compás, y se generó un registro audiovisual que sirve tanto para fines documentales como de investigación. Parte de la tesis se centró en descubrir y analizar patrones rítmicos a partir de grabaciones de audio. Se diseñó una representación en forma de mapa de patrones rítmicos basada en características espectrales. El tipo de análisis que se puede realizar con los métodos propuestos se ilustra con algunos experimentos. La tesis también abordó de forma sistemática (y por primera vez) el estudio y la caracterización de las propiedades micro rítmicas del candombe. Los resultados sugieren que las micro desviaciones temporales son un componente estructural del ritmo, dando lugar a una especie de "swing" característico. El resto de la tesis se dedicó a la inferencia automática de la estructura métrica a partir de grabaciones de audio. Se propuso un esquema Bayesiano supervisado para el seguimiento de patrones rítmicos, del cual se disponibilizó públicamente una implementación de software. Los resultados dan evidencia adicional de la capacidad de generalización del enfoque Bayesiano a ritmos complejos. Por último, la detección de inicio de compás se formuló como un problema de compresión de datos. Esto resultó en un método novedoso que demostró ser efectivo para una buena parte de los datos y abre varias líneas de investigación

Composition portfolio : producing techno grooves

PhD Thesis Additional music to be consulted in Robinson Library only.PhD submission consisting of a portfolio of recordings presented both in their published form on five twelve-inch vinyl records and on two compact discs. The portfolio is accompanied by a commentary intended to facilitate access to the aesthetic statement presented in the portfolio. Following Charles Keil’s distinction between ‘embodied meaning’ and ‘engendered feeling’ this project investigates approaches to the creation of Techno music in terms of the significance of specific sounds, techniques and technologies in generating subsyntactic value. The concept of the groove and the importance of microtiming as they operate in my practice are discussed in the commentary and demonstrated through the production of a series of Techno records including both original compositions and remixes

Toward Interactive Music Generation: A Position Paper

Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations

Functional Scaffolding for Musical Composition: A New Approach in Computer-Assisted Music Composition

While it is important for systems intended to enhance musical creativity to define and explore musical ideas conceived by individual users, many limit musical freedom by focusing on maintaining musical structure, thereby impeding the user\u27s freedom to explore his or her individual style. This dissertation presents a comprehensive body of work that introduces a new musical representation that allows users to explore a space of musical rules that are created from their own melodies. This representation, called functional scaffolding for musical composition (FSMC), exploits a simple yet powerful property of multipart compositions: The pattern of notes and rhythms in different instrumental parts of the same song are functionally related. That is, in principle, one part can be expressed as a function of another. Music in FSMC is represented accordingly as a functional relationship between an existing human composition, or scaffold, and an additional generated voice. This relationship is encoded by a type of artificial neural network called a compositional pattern producing network (CPPN). A human user without any musical expertise can then explore how these additional generated voices should relate to the scaffold through an interactive evolutionary process akin to animal breeding. The utility of this insight is validated by two implementations of FSMC called NEAT Drummer and MaestroGenesis, that respectively help users tailor drum patterns and complete multipart arrangements from as little as a single original monophonic track. The five major contributions of this work address the overarching hypothesis in this dissertation that functional relationships alone, rather than specialized music theory, are sufficient for generating plausible additional voices. First, to validate FSMC and determine whether plausible generated voices result from the human-composed scaffold or intrinsic properties of the CPPN, drum patterns are created with NEAT Drummer to accompany several different polyphonic pieces. Extending the FSMC approach to generate pitched voices, the second contribution reinforces the importance of functional transformations through quality assessments that indicate that some partially FSMC-generated pieces are indistinguishable from those that are fully human. While the third contribution focuses on constructing and exploring a space of plausible voices with MaestroGenesis, the fourth presents results from a two-year study where students discuss their creative experience with the program. Finally, the fifth contribution is a plugin for MaestroGenesis called MaestroGenesis Voice (MG-V) that provides users a more natural way to incorporate MaestroGenesis in their creative endeavors by allowing scaffold creation through the human voice. Together, the chapters in this dissertation constitute a comprehensive approach to assisted music generation, enabling creativity without the need for musical expertise

Using humanoid robots to study human behavior

Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans “program” behavior in-or train-each other

R-VAE: Live latent space drum rhythm generation from minimal-size datasets

In this article, we present R-VAE, a system designed for the modeling and exploration of latent spaces learned from rhythms encoded in MIDI clips. The system is based on a variational autoencoder neural network, uses a data structure that is capable of encoding rhythms in simple and compound meter, and can learn models from little training data. To facilitate the exploration of models, we implemented a visualizer that relies on the dynamic nature of the pulsing rhythmic patterns. To test our system in real-life musical practice, we collected small-scale datasets of contemporary music genre rhythms and trained models with them. We found that the non-linearities of the learned latent spaces coupled with tactile interfaces to interact with the models were very expressive and led to unexpected places in musical composition and live performance settings. A music album was recorded and it was premiered at a major music festival using the VAE latent space on stage