Musical Haptics

Haptic Musical Instruments; Haptic Psychophysics; Interface Design and Evaluation; User Experience; Musical Performanc

Musical Haptics

Haptic Musical Instruments; Haptic Psychophysics; Interface Design and Evaluation; User Experience; Musical Performanc

Musical Haptics

This Open Access book offers an original interdisciplinary overview of the role of haptic feedback in musical interaction. Divided into two parts, part I examines the tactile aspects of music performance and perception, discussing how they affect user experience and performance in terms of usability, functionality and perceived quality of musical instruments. Part II presents engineering, computational, and design approaches and guidelines that have been applied to render and exploit haptic feedback in digital musical interfaces. Musical Haptics introduces an emerging field that brings together engineering, human-computer interaction, applied psychology, musical aesthetics, and music performance. The latter, defined as the complex system of sensory-motor interactions between musicians and their instruments, presents a well-defined framework in which to study basic psychophysical, perceptual, and biomechanical aspects of touch, all of which will inform the design of haptic musical interfaces. Tactile and proprioceptive cues enable embodied interaction and inform sophisticated control strategies that allow skilled musicians to achieve high performance and expressivity. The use of haptic feedback in digital musical interfaces is expected to enhance user experience and performance, improve accessibility for disabled persons, and provide an effective means for musical tuition and guidance

Tangibility and Richness in Digital Musical Instrument Design

PhDThe sense of touch plays a fundamental role in musical performance: alongside hearing, it is the primary sensory modality used when interacting with musical instruments. Learning to play a musical instrument is one of the most developed haptic cultural practices, and within acoustic musical practice at large, the importance of touch and its close relationship to virtuosity and expression is well recognised. With digital musical instruments (DMIs) – instruments involving a combination of sensors and a digital sound engine – touch-mediated interaction remains the foremost means of control, but the interfaces of such instruments do not yet engage with the full spectrum of sensorimotor capabilities of a performer. This poses compelling questions for digital instrument design: how does the nuance and richness of physical interaction with an instrument manifest itself in the digital domain? Which design parameters are most important for haptic experience, and how do these parameters affect musical performance? Built around three practical studies which utilise DMIs as technology probes, this thesis addresses these questions from the point of view of design, of empirical musicology, and of tangible computing. In the first study musicians played a DMI with continuous pitch control and vibrotactile feedback in order to understand how dynamic tactile feedback can be implemented and how it influences musician experience and performance. The results suggest that certain vibrotactile feedback conditions can increase musicians’ tuning accuracy, but also disrupt temporal performance. The second study examines the influence of asynchronies between audio and haptic feedback. Two groups of musicians, amateurs and professional percussionists, were tasked with performing on a percussive DMI with variable action-sound latency. Differences between the two groups in terms of temporal accuracy and quality judgements illustrate the complex effects of asynchronous multimodal feedback. In the third study guitar-derivative DMIs with variable levels of control richness were observed with non-musicians and guitarists. The results from this study help clarify the relationship between tangible design factors, sensorimotor expertise and instrument behaviour. This thesis introduces a descriptive model of performer-instrument interaction, the projection model, which unites the design investigations from each study and provides a series of reflections and suggestions on the role of touch in DMI design.Doctoral Training Centre for Media and Arts Technolog

Multisensory learning in adaptive interactive systems

The main purpose of my work is to investigate multisensory perceptual learning and sensory integration in the design and development of adaptive user interfaces for educational purposes. To this aim, starting from renewed understanding from neuroscience and cognitive science on multisensory perceptual learning and sensory integration, I developed a theoretical computational model for designing multimodal learning technologies that take into account these results. Main theoretical foundations of my research are multisensory perceptual learning theories and the research on sensory processing and integration, embodied cognition theories, computational models of non-verbal and emotion communication in full-body movement, and human-computer interaction models. Finally, a computational model was applied in two case studies, based on two EU ICT-H2020 Projects, "weDRAW" and "TELMI", on which I worked during the PhD

Violin Augmentation Techniques for Learning Assistance

PhDLearning violin is a challenging task requiring execution of pitch tasks with the left hand using a strong aural feedback loop for correctly adjusting pitch, concurrent with the right hand moving a bow precisely with correct pressure across strings. Real-time technological assistance can help a student gain feedback and understanding helpful for learning and maintaining motivation. This thesis presents real-time low-cost low-latency violin augmentations that can be used to assist learning the violin along with other real-time performance tasks. To capture bow performance, we demonstrate a new means of bow tracking by measuring bow hair de ection from the bow hair being pressed against the string. Using near- eld optical sensors placed along the bow we are able to estimate bow position and pressure through linear regression from training samples. For left hand pitch tracking, we introduce low cost means for tracking nger position and illustrate the combination of sensed results with audio processing to achieve high accuracy low-latency pitch tracking. We subsequently verify our new tracking methods' e ectiveness and usefulness demonstrating low-latency note onset detection and control of real-time performance visuals. To help tackle the challenge of intonation, we used our pitch estimation to develop low latency pitch correction. Using expert performers, we veri ed that fully correcting pitch is not only disconcerting but breaks a violinist's learned pitch feedback loop resulting in worse asplayed performance. However, partial pitch correction, though also linked to worse as-played performance, did not lead to a signi cantly negative experience con rming its potential for use to temporarily reduce barriers to success. Subsequently, in a study with beginners, we veri ed that when the pitch feedback loop is underdeveloped, automatic pitch correction did not signi cantly hinder performance, but o ered an enjoyable low-pitch error experience and that providing an automatic target guide pitch was helpful in correcting performed pitch error

Physical modelling meets machine learning: performing music with a virtual string ensemble

This dissertation describes a new method of computer performance of bowed string instruments (violin, viola, cello) using physical simulations and intelligent feedback control. Computer synthesis of music performed by bowed string instruments is a challenging problem. Unlike instruments whose notes originate with a single discrete excitation (e.g., piano, guitar, drum), bowed string instruments are controlled with a continuous stream of excitations (i.e. the bow scraping against the string). Most existing synthesis methods utilize recorded audio samples, which perform quite well for single-excitation instruments but not continuous-excitation instruments. This work improves the realism of synthesis of violin, viola, and cello sound by generating audio through modelling the physical behaviour of the instruments. A string's wave equation is decomposed into 40 modes of vibration, which can be acted upon by three forms of external force: A bow scraping against the string, a left-hand finger pressing down, and/or a right-hand finger plucking. The vibration of each string exerts force against the instrument bridge; these forces are summed and convolved with the instrument body impulse response to create the final audio output. In addition, right-hand haptic output is created from the force of the bow against the string. Physical constants from ten real instruments (five violins, two violas, and three cellos) were measured and used in these simulations. The physical modelling was implemented in a high-performance library capable of simulating audio on a desktop computer one hundred times faster than real-time. The program also generates animated video of the instruments being performed. To perform music with the physical models, a virtual musician interprets the musical score and generates actions which are then fed into the physical model. The resulting audio and haptic signals are examined with a support vector machine, which adjusts the bow force in order to establish and maintain a good timbre. This intelligent feedback control is trained with human input, but after the initial training is completed the virtual musician performs autonomously. A PID controller is used to adjust the position of the left-hand finger to correct any flaws in the pitch. Some performance parameters (initial bow force, force correction, and lifting factors) require an initial value for each string and musical dynamic; these are calibrated automatically using the previously-trained support vector machines. The timbre judgements are retained after each performance and are used to pre-emptively adjust bowing parameters to avoid or mitigate problematic timbre for future performances of the same music. The system is capable of playing sheet music with approximately the same ability level as a human music student after two years of training. Due to the number of instruments measured and the generality of the machine learning, music can be performed with ensembles of up to ten stringed instruments, each with a distinct timbre. This provides a baseline for future work in computer control and expressive music performance of virtual bowed string instruments

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space