Raymond Vincent Migneco All Rights Reserved

Analysis and Synthesis of Expressive Guitar Performance Raymond Vincent Migneco Advisor: Youngmoo Edmund Kim, Ph.D. The guitar is one of the most popular and versatile instruments used in Western music cultures. Dating back to the Renaissance era, the guitar can be heard in nearly every genre of Western music, and is arguably the most widely used instrument in present-day rock music. Over the span of 500 years, the guitar has developed a multitude of performance and compositional styles associated with nearly every musical genre such as classical, jazz, blues and rock. This versatility can be largely attributed to the relatively simplistic nature of the instrument, which can be built from a variety of materials and optionally amplified. Furthermore, the flexibility of the instrument allows performers to develop unique playing styles, which reflect how they articulate the guitar to convey certain musical expressions. Over the last three decades, physicaland physically-inspired models of musical instruments have emerged as a popular methodology for modeling and synthesizing various instruments, including the guitar. These models are popular since their components relate to the actual mechanisms involved with sound production on a particular instrument, such as the vibration of a guitar string. Since the control parameters are physically relevant, they have a variety of applications including control and manipulation of “virtual instruments.” The focus of much of the literature on physical modeling for guitars is concerned with calibrating the models from recorded tones to ensure that the behavior of real strings is captured. However, far less emphasis is placed on extracting parameters that pertain to the expressive styles of the guitarist. This research presents techniques for the analysis and synthesis of plucked guitar tones that are capable of modeling the expressive intentions applied through the guitarist’s articulation during performance. A joint source-filter estimation approach is developed to account for the performer’s articulation and the corresponding resonant string response. A data-driven, statistical approach for modeling the source signals is also presented in order to capture the nuances of particular playing styles. This research has several pertinent applications, including the development of expressive synthesizers for new musical interfaces and the characterization of performance through audio analysis. Copyright 2012 Raymond Vincent Migneco All Rights Reserved Copyright 2012 Raymond Vincent Migneco All Rights Reserved 1 CHAPTER 1: INTRODUCTION The guitar is one of the most popular and versatile instruments used in Western music cultures. Dating back to the Renaissance period, it has been incorporated into nearly every genre of Western music and, hence, has a rich tradition of design and performance techniques pertaining to each genre. From a cultural standpoint, musicians and non-musicians alike are captivated by the performances of virtuoso guitarists past and present, who introduced innovative techniques that defined or redefined the way the instrument was played. This deep appreciation is no doubt related to the instrument’s adaptability, as it is recognized as a primary instrument in many genres, such as blues, jazz, folk, country and rock. The guitar’s versatility is inherent in its simple design, which can be attributed to its use in multiple musical genres. The basic components of any guitar consist of a set of strings mounted across a fingerboard and a resonant body to amplify the vibration of the strings. The tension on each string is adjusted to achieve a desired pitch when the string is played. Particular pitches are produced by c amping down each string at a specific location along the fingerboard, which changes the e↵ective length of the string and, thus, the associated pitch when it is plucked. Frets, which are metallic strips spanning the width of the fingerboard, are usually installed on the fingerboard to exactly specify the location of notes in accordance with an equal tempered division of the octave. The basic design of the guitar has been augmented in a multitude of ways to satisfy the demands of di↵erent performers and musical genres. For example, classical guitars are strung with nylon strings, which can be played with the fingers or nails, and a wide fingerboard to permit playing scales and chords with minimal interference from adjacent strings. Often a solo instrument, the classical guitar requires a resonant body for amplification where the size and materials of the body are chosen to achieve a specific timbre. On the other hand, country and folk guitarists prefer steelstrings which generally produce “brighter” tones. Electric guitars are designed to accommodate the demands of guitarists performing rock, blues and jazz music. These guitars are outfitted with electromagnetic pickups where string vibration induces an electrical current, which can be processed to apply certain e↵ects (e.g. distortion, reverberation) and eventually amplified. The role of the body is less important for electric guitars (although guitarists argue that it a↵ects the instrument’s Copyright 2012 Raymond Vincent Migneco All Rights Reserved 2 timbre) where the body is generally thinner to increase comfort during performance. When the electric guitar is outfitted with light gauge strings, it facilitates certain techniques such as pitchbending and legato, which are more di cult to perform on acoustic instruments. Though the guitar can be designed and played in di↵erent ways to achieve a vast tonal palette, the underlying physical principles of vibrating strings is constant for each variation of the instrument. Consequently, a popular topic among musicians and researchers is the development of quantitative guitar models that simulate this behavior. Physicaland physically-inspired models of musical instruments have emerged as a popular methodology for this task. The lure of these models is that they simulate the physical phenomena responsible for sound production in instruments, such as a vibrating strings or air in a column, and produce high-quality synthetic tones. Properly calibrating these models, however, remains a di cult task and is an on-going topic in the literature. Several guitar synthesizers have been developed using physically-inspired models, such as waveguide synthesis and the Karplus-Strong Algorithm. In the last decade, there has been considerable interest in digitally modeling analog guitar components and e↵ects using digital signal processing (DSP) techniques. This work is highly relevant to the consumer electronics industry since it promises low-cost, digital “clones” of vintage, analog equipment. The promise of these devices is to help musicians consolidate their analog equipment into a single device or acquire the specific tones and capabilities of expensive and/or discontinued equipment at lower cost. Examples of products designed using this technology include Line6 modeling guitars and amplifiers, where DSP is used to replicate the sounds of well-known guitars and tube-based amplifiers [45, 46]. Despite the large amount of research focused on digitally modeling the physics of the guitar and its associated e↵ects, there has been relatively little research conducted which analyzes the expressive attributes of guitar performance. The current research is mainly concerned with implementing specific performance techniques into physical models based on detailed physical analysis of the performer-instrument interaction. However, there is a void in the research for guitar modeling and synthesis that is concerned with measuring physical and expressive data from recordings. Obtaining such data is essential for developing an expressive guitar synthesizer ; that is, a system that not only faithfully replicates guitar timbres, but is also capable of simulating expressive intentions used by many guitarists. Copyright 2012 Raymond Vincent Migneco All Rights Reserved 3