Playing Technique and Violin Timbre: Detecting Bad Playing

For centuries, luthiers have committed to working towards better understanding and improving the sound characteristics and playability of violins. With advances in technology and signal processing, studies attempting to define a violin’s sound qualityvia physical characteristics and resonance patterns have ensued. Existing work has primarily focused on physical aspects reflecting an instrument’s sound quality. In the music information retrieval domain, advances have been made in areas suchas instrument identification tasks. Although much research has been completed on finding suitable features from which musical instruments can be represented, little work has focused on the violin’s complete timbre space and the effect a player has on the sound produced. This thesis specifically focuses on representing violin timbre such that a computer can detect the sound associated with a beginner from that of a professional standard player and detect typical beginner playing faults based on analysis of thewaveform signal only. Work has been limited to nine playing faults considered by professional musicians to be typical of beginner violinists. In order to achieve these goals, it was necessary to create a suitable dataset consisting of an equal number of beginner and professional standard legato notesamples. Feature extraction was then carried out by taking features from the time, spectral and cepstral domains. Selected features were then used to represent the samples in a classifier based on their efficacy at reflecting change within the violin’s timbrespace. The dataset underwent the scrutiny of professional standard stringed instrumentplayers via listening tests from which the target audience’s perception was captured. This information was verified and normalised before use as a priori labels in the classifier. Based on different feature representations, classification of violin notesreflecting perceived sound quality is presented in this thesis. The results show that it is possible to get a computer to determine between beginner and professional standard player legato notes and to detect playing faults. This thesis involves a thoroughunderstanding of violin playing, its perception, suitable analysis methods, feature extraction, representation and classification

Analog Violin Audio Synthesizer

Abstract In the past decade, music electronics have almost completely shifted from analog to digital technology. Digital keyboards and effects provide more sound capabilities than their analog predecessors, while also reducing size and cost. However, many musicians still prefer analog instruments due to the perception that they produce superior sound quality. Many musicians spend extra money and accommodate the extra space required for analog technologies instead of digital. Furthermore, audio synthesizers are commonly controlled with the standard piano keyboard interface. Many musicians can perform sufficiently on a keyboard, but requiring a specific skill set limits the size of the market for a product. Also, when reproducing instruments such as a violin, a keyboard will not suffice in simulating a controllable vibrato from a fretless fingerboard. There is a need for an interface that allows the user to successfully reproduce the sound of the desired instrument. The violin is just one example of instruments that cannot be completely reproduced on a keyboard. For example, cellos, trombones and slide guitars all have features that a keyboard cannot simulate in real time. The Analog Violin Synthesizer uses oscillators and analog technology to reproduce the sound of a violin. The user controls the synthesizer with a continuous touch sensor, representing the fretless violin fingerboard. The continuous interface allows for a violin sound played as a standard note, or a warmer sound with adjustable vibrato, based on how the user moves his or her hand. This product provides an innovation and next step to the use of analog technology in sound synthesis. However, as digital technology continues to improve, this product could potentially cross over into digital, with the continued use of the touch interface. Currently, there are products that utilize touch input, however they are often used for sound effects, and atmospheric sounds. Rarely are they used to allow for the digital playability of a synthesized acoustic instrument

Probabilistic characterization and synthesis of complex driven systems

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.Includes bibliographical references (leaves 194-204).Real-world systems that have characteristic input-output patterns but don't provide access to their internal states are as numerous as they are difficult to model. This dissertation introduces a modeling language for estimating and emulating the behavior of such systems given time series data. As a benchmark test, a digital violin is designed from observing the performance of an instrument. Cluster-weighted modeling (CWM), a mixture density estimator around local models, is presented as a framework for function approximation and for the prediction and characterization of nonlinear time series. The general model architecture and estimation algorithm are presented and extended to system characterization tools such as estimator uncertainty, predictor uncertainty and the correlation dimension of the data set. Furthermore a real-time implementation, a Hidden-Markov architecture, and function approximation under constraints are derived within the framework. CWM is then applied in the context of different problems and data sets, leading to architectures such as cluster-weighted classification, cluster-weighted estimation, and cluster-weighted sampling. Each application relies on a specific data representation, specific pre and post-processing algorithms, and a specific hybrid of CWM. The third part of this thesis introduces data-driven modeling of acoustic instruments, a novel technique for audio synthesis. CWM is applied along with new sensor technology and various audio representations to estimate models of violin-family instruments. The approach is demonstrated by synthesizing highly accurate violin sounds given off-line input data as well as cello sounds given real-time input data from a cello player.by Bernd Schoner.Ph.D

Machine Learning of Musical Gestures: Principles and Review

We present an overview of machine learning (ML) techniques and their application in interactive music and new digital instrument design. We first provide the non-specialist reader an introduction to two ML tasks, classification and regression, that are particularly relevant for gestural interaction. We then present a review of the literature in current NIME research that uses ML in musical gesture analysis and gestural sound control. We describe the ways in which machine learning is useful for creating expressive musical interaction, and in turn why live music performance presents a pertinent and challenging use case for machine learning

The acoustics of the violin: a review.

To understand the design and function of the violin requires investigation of a range of scientific questions. This paper presents a review: the relevant physics covers the nonlinear vibration of a bowed string, the vibration of the instrument body, and the consequent sound radiation. Questions of discrimination and preference by listeners and players require additional studies using the techniques of experimental psychology, and these are also touched on in the paper. To address the concerns of players and makers of instruments requires study of the interaction of all these factors, coming together in the concept of 'playability' of an instrument.This is the author accepted manuscript. The final version is available from IOP Science at http://iopscience.iop.org/0034-4885/77/11/115901

AXMEDIS 2007 Conference Proceedings

The AXMEDIS International Conference series has been established since 2005 and is focused on the research, developments and applications in the cross-media domain, exploring innovative technologies to meet the challenges of the sector. AXMEDIS2007 deals with all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, interoperability, protection and rights management. It addresses the latest developments and future trends of the technologies and their applications, their impact and exploitation within academic, business and industrial communities

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