Bowing models for string players

A bowing is a sequence of bow motions that enable a piece of music to be played on a string instrument with an appropriate interpretation and sound. Traditional notation shows only the bow direction for a few notes. We propose a bowing model, which contains information about the bowings of all the notes, and we show how the bowing model can be represented in software and describe how algorithms can use it to perform several tasks that help the performer. The model, and the algorithms, are suitable both for offline editing of music and for presentation on an electronic display during performance. In particular, software can show or hide bowings on various notes, according to the performer's needs; it can calculate a full bowing; it can modify a bowing based on preferences indicated by the performer; and it can allow bowings to be archived and searched. This approach is not prescriptive: the performer is in full control of all artistic decisions, while the software carries out repetitive tasks

Bowing Models for String Players

ABSTRACT A bowing is a sequence of bow motions that enable a piece of music to be played on a string instrument with an appropriate interpretation and sound. Traditional notation shows only the bow direction for a few notes. We propose a bowing model, which contains information about the bowings of all the notes, and we show how the bowing model can be represented in software and describe how algorithms can use it to perform several tasks that help the performer. The model, and the algorithms, are suitable both for offline editing of music and for presentation on an electronic display during performance. In particular, software can show or hide bowings on various notes, according to the performer's needs; it can calculate a full bowing; it can modify a bowing based on preferences indicated by the performer; and it can allow bowings to be archived and searched. This approach is not prescriptive: the performer is in full control of all artistic decisions, while the software carries out repetitive tasks

Bridging the divide : embedding voice-leading analysis in string pedagogy and performance.

Experience as a music lecturer in higher/further education and as an instrumental teacher suggests that instrumental pedagogy – focused on strings – and music analysis could usefully be brought closer together to enhance performance. The benefits of linkage include stimulating intellectual enquiry and creative interpretation, as well as honing improvisatory skills; voice-leading analysis, particularly, may even aid technical issues of pitching, fingering, shifting and bowing. This article details an experimental curriculum, entitled ‘Voice-leading for Strings’, which combines voice-leading principles with approaches to string teaching developed from Nelson, Rolland and Suzuki, supplemented by Kodály's hand-signs. Findings from informal trials at Lancaster University (1995–7), which also adapted material for other melody instruments and keyboard, strongly support this perceived symbiotic relationship

‘Old style’ Cape Breton fiddling : narrative, interstices, dancing

Publisher PD

Driving the Bow

Publisher PD

A methodology for investigation of bowed string performance through measurement of violin bowing technique

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (leaves 181-186).Virtuosic bowed string performance in many ways exemplifies the incredible potential of human physical performance and expression. Today, a great deal is known about the physics of the violin family and those factors responsible for its sound capabilities. However, there remains much to be discovered about the intricacies of how players control these instruments in order to achieve their characteristic range and nuance of sound. Today, technology offers the ability to study this player control under realistic, unimpeded playing conditions to lead to greater understanding of these performance skills. Presented here is a new methodology for investigation of bowed string performance that uses a playable hardware measurement system to capture the gestures of right hand violin bowing technique. Building upon previous Hyperstring research, this measurement system was optimized to be small, lightweight, and portable and was installed on a carbon fiber violin bow and an electric violin to enable study of realistic, unencumbered violin performances. Included in the system are inertial and force sensors, and an electric field position sensor. In order to maximize the applicability of the gesture data provided by this system to related fields of interest, all of the sensors were calibrated in SI units.(cont.) The gesture data captured by these sensors are recorded together with the audio data from the violin as they are produced by violinists in typical playing scenarios. To explore the potential of the bowing measurement system created, a study of standard bowing techniques, such as detache, martele and spiccato, was conducted with expert violinist participants. Gesture data from these trials were evaluated and input to a classifier to examine physical distinctions between bowing techniques, as well as between players. Results from this analysis, and their implications on this methodology will be presented. In addition to this examination of bowing techniques, applications of the measurement system for study of bowed string acoustics and digital music instrument performance, with focus on virtual instruments created from physical models, will be discussed.by Diana Young.Ph.D

Spatial distribution of surface EMG on trapezius and lumbar muscles of violin and cello players in single note playing

Musicians activate their muscles in different patterns, depending on their posture, the instrument being played, and their experience level. Bipolar surface electrodes have been used in the past to monitor such activity, but this method is highly sensitive to the location of the electrode pair. In this work, the spatial distribution of surface EMG (sEMG) of the right trapezius and right and left erector spinae muscles were studied in 16 violin players and 11 cello players. Musicians played their instrument one string at a time in sitting position with/without backrest support. A 64 sEMG electrode (16x4) grid, 10mm inter-electrode distance (IED), was placed over the middle and lower trapezius (MT and LT) of the bowing arm. Two 16x2 electrode grids (IED=10mm) were placed on the left and right erector spinae muscles. Subjects played each of the four strings of the instrument either in large (1bow/s) or detaché tip/tail (8bows/s) bowing in two sessions (two days). In each of two days, measurements were repeated after half an hour of exercise to see the effect of exercise on the muscle activity and signal stability. A “muscle activity index” (MAI) was defined as the spatial average of the segmented active region of the RMS map. Spatial maps were automatically segmented using the watershed algorithm and thresholding. Results showed that, for violin players, sliding the bow upward from the tip toward the tail results in a higher MAI for the trapezius muscle than a downward bow. On the contrary, in cello players, higher MAI is produced in the tail to tip movement. For both instruments, an increasing MAI in the trapezius was observed as the string position became increasingly lateral, from string 1 (most medial) toward string 4 (most lateral). Half an hour of performance did not cause significant differences between the signal quality and the MAI values measured before and after the exercise. The MAI of the left and right erector spinae was smaller in the case of backrest support, especially for violin players. Back muscles of violin and cello players were activated asymmetrically, specifically in fast movements (detaché tip/tail). These findings demonstrate the sensitivity and stability of the technique and justify more extensive investigation following this proof of concept

Estimation of load sharing among muscles acting on the same joint and Applications of surface electromyography

The force produced by a specific muscle cannot be measured and what is measured is the total force provided by all the active muscles acting on a joint, including agonists and antagonists. The first part of this work (chapter 3) addresses the issue of load sharing by proposing two possible approaches and testing them. The second part (chapter 4 and 5) addresses two applications of surface EMG focusing on the study of a) muscle relaxation associated to Yoga sessions and b) the activation of muscle of the back and shoulder of musicians playing string instruments (violin, viola and cello). In both parts the element of innovation is the use of two dimensional electrode arrays and of techniques based on EMG Imaging. The objectives of this work are presented and explained in chapter 1 while the basic concepts of surface EMG are summarized in chapter 2. Different EMG-based muscle force models found in the literature are explained and discussed. Two renowned amplitude indicators in surface EMG (sEMG) studies are the average rectified value (ARV) and the root mean square (RMS). These two amplitude indicators are computed over a defined time window of the recorded signals to represent the muscle activity. The advantages and disadvantages of RMS and ARV are compared and discussed for a simple sinusoid as well as for more complex signals (simulated motor unit action potential detected by high density electrode grid). The results show that RMS is more robust to the sampling frequency than ARV. In this thesis, starting from the simulation of a single fiber and of a group of fibers (motor unit), it is shown that inter electrode distance (IED) greater than10 mm causes aliasing. Aliasing is a source of error in sEMG map interpretation or decisions that are made by automatic algorithms such as those providing image segmentation for the identifications of regions of interest. Chapter 2 discusses three segmentation algorithms (K-means, h-dome, watershed) and compares them in order to find the most suitable method. Results reveal that among the three mentioned algorithms, watershed provides most accurate segmentation for the simulated ARV maps. Chapter 3 presents a mathematical model that is associated to the monotonic Force-EMG relation. A possible non-linear relationship between the EMG and force or torque is presented. A system of "M" equations is obtained by performing "M" measurements at "M" different force levels in isometric conditions. The solutions of such system of equations are the values for each muscles. Two different approaches were investigated for finding the solutions of the system, which are: a) Analytical-Graphical Approach (AGA) and b) Numerical Approach (NA) consisting of error minimization (between the total estimated and measured force) applying optimization algorithms. The AGA was used to find the model parameters of each muscle contributing to the force production on a joint by finding the intersection of those surfaces that can be obtained from sequential substitutions of the model parameters in the equations corresponding to each contraction level. In simulation studies, the AGA graphically shows that there is more than one solution to the load sharing problem even for the simplest theoretical case (i.e. a joint spanned by only two muscles). The second approach, based on minimization of the mean square error between the measured and the total estimated force or torque (with "N" muscles involved) provides an estimate of the model parameters that in turn provides the force contributions of the individual muscles. The optimization algorithms can find the solutions of our system made of non-linear equations (see chapter 3). Starting from different point (initial conditions), different solutions can be found, as predicted by the AGA approach for the two-muscle case. The main conclusion of this study is that the load sharing strategy is not unique. Chapter 4 discusses the application of surface electromyography to a single case study of Yoga relaxation to show the feasibility of measurements. The effect of yoga relaxation on muscle activity (sEMG amplitude), as well as on mean and median frequencies and muscle fiber's conduction Velocity, is discussed in this chapter. No changes in the sEMG activity pattern distribution were found for the same task performed before and after applying yoga relaxation technique. However, myoelectric manifestations of fatigue were smaller after relaxation and returned to the normal pattern after the recovery phase from relaxation. Further studies are justified. Chapter 5 describes results and discusses the spatial distribution of muscle activity over the Trapezius and Erector Spinae muscles of musicians playing string instruments. In chapter 5, the effect of backrest support in sitting position during playing cello, viola, and violin on the muscle activity index of upper and lower Trapezius muscle of the bowing arm, upper Trapezius muscle of non-bowing arm, left and right Erector Spinae muscles is investigated. Two professional players (one cello and one viola) and five student players (one cello, three violin and one viola) participated in this study. The muscle activity index (MAI) was defined as the spatial average of RMS values of the muscle active region detected by watershed segmentation for Trapezius muscles (left and right), and thresholding technique (70% of the maximum value) for left and right Erector Spinae muscles. It was found that the MAI is string (note) dependent. Statistical difference (p < 0:05) between the MAIs of left Erector Spinae muscle during playing with and without backrest support was observed in four (out of five) student players. No statistical differences were observed on the muscle activity of Trapezius (bowing and no-bowing arms) during playing with and without backrest support in different types of bowing for all musicians. In conclusion, this work addresses a) the issue of spatial sampling and segmentation of sEMG using 2D electrode arrays, b) two possible approaches to the load-sharing issue, c) a single-case study of Yoga relaxation and d) the distribution of muscle activity above the Trapezius and Erector Spinae muscles of musicians playing string instruments. Previously unavailable knowledge has been achieved in all these four studies