Gesture Prediction Model for the Guitar Fingering Problem

In this thesis we provide a method for finding the fingering of a music piece on any type of guitar using a hand model. Adapting to the real world conditions by deploying a model of the user's hand, and considering the constraints of the guitar and the music notes is what makes our method more realistic. We have modeled the movements of the user's hand in such a way that the thumb does not play any role, and the movements of the other four fingers are modeled using a set of kinematics equations. We use two sets of constraints derived from the guitar and the music notes in order to include the playing techniques, which are required by the music piece and the guitar. The guitar is considered to be a separate entity in our model having its own properties, resulting in a method independent of the type and tuning of the instrument. Since we are using the hand model for generating the fingering of the music piece, the results of the method are gestures generated for the notes, and the final outcome will be an animation for the entire sheet of music.  M.S

A Differential Evolution Algorithm Assisted by ANFIS for Music Fingering

Music fingering is a cognitive process whose goal is to map each note of a music score to a fingering on some instrument. A fingering specifies the fingers of the hands that the player should use to play the notes. This problem arises for many instruments and it can be quite different from instrument to instrument; guitar fingering, for example, is different from piano fingering. Previous work focuses on specific instruments, in particular the guitar, and evolutionary algorithms have been used. In this paper, we propose a differential evolution (DE) algorithm designed for general music fingering (any kind of music instruments). The algorithm uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) engine that learns the fingering from music already fingered. The algorithm follows the basic DE strategy but exploits also some customizations specific to the fingering problem. We have implemented the DE algorithm in Java and we have used the ANFIS network in Matlab. The two systems communicate by using the MatlabControl library. Several tests have been performed to evaluate its efficacy

畳み込みニューラルネットワークと時空間サポートベクター回帰に基づくギターの運指の自動評価システム

早大学位記番号:新7965早稲田大

Modeling Bends in Popular Music Guitar Tablatures

Tablature notation is widely used in popular music to transcribe and share guitar musical content. As a complement to standard score notation, tablatures transcribe performance gesture information including finger positions and a variety of guitar-specific playing techniques such as slides, hammer-on/pull-off or bends.This paper focuses on bends, which enable to progressively shift the pitch of a note, therefore circumventing physical limitations of the discrete fretted fingerboard. In this paper, we propose a set of 25 high-level features, computed for each note of the tablature, to study how bend occurrences can be predicted from their past and future short-term context. Experiments are performed on a corpus of 932 lead guitar tablatures of popular music and show that a decision tree successfully predicts bend occurrences with an F1 score of 0.71 anda limited amount of false positive predictions, demonstrating promising applications to assist the arrangement of non-guitar music into guitar tablatures

Music Information Retrieval Meets Music Education

This paper addresses the use of Music Information Retrieval (MIR) techniques in music education and their integration in learning software. A general overview of systems that are either commercially available or in research stage is presented. Furthermore, three well-known MIR methods used in music learning systems and their state-of-the-art are described: music transcription, solo and accompaniment track creation, and generation of performance instructions. As a representative example of a music learning system developed within the MIR community, the Songs2See software is outlined. Finally, challenges and directions for future research are described

AutoRhythmGuitar: Computer-aided Composition for Rhythm Guitar in the Tab Space

(Abstract to follow

Biomechanical Modelling of Musical Performance: A Case Study of the Guitar

Merged with duplicate record 10026.1/2517 on 07.20.2017 by CS (TIS)Computer-generated musical performances are often criticised for being unable to match the expressivity found in performances by humans. Much research has been conducted in the past two decades in order to create computer technology able to perform a given piece music as expressively as humans, largely without success. Two approaches have been often adopted to research into modelling expressive music performance on computers. The first focuses on sound; that is, on modelling patterns of deviations between a recorded human performance and the music score. The second focuses on modelling the cognitive processes involved in a musical performance. Both approaches are valid and can complement each other. In this thesis we propose a third complementary approach, focusing on the guitar, which concerns the physical manipulation of the instrument by the performer: a biomechanical approach. The essence of this thesis is a study on capturing, analyzing and modelling information about motor and biomechanical processes of guitar performance. The focus is on speed, precision, and force of a guitarist's left-hand. The overarching questions behind our study are: 1) Do unintentional actions originating from motor and biomechanical functions during musical performance contribute a material "human feel" to the performance? 2) Would it be possible determine and quantify such unintentional actions? 3) Would it be possible to model and embed such information in a computer system? The contributionst o knowledgep ursued in this thesis include: a) An unprecedented study of guitar mechanics, ergonomics, and playability; b) A detailed study of how the human body performs actions when playing the guitar; c) A methodologyt o formally record quantifiable data about such actionsin performance; d) An approach to model such information, and e) A demonstration of how the above knowledge can be embeddedin a system for music performance

The Net of Jewels: An exploration and shape-based guitar pedagogy for songwriters

This article offers an informal, summary description of the Net of Jewels (NOJ) method, a guitar pedagogy specifically targeted to songwriters writing songs on guitar. The core of the NOJ approach is a progressive sequence of activities that incorporate shape-directed exploration of the guitar fingerboard as an integral aspect, supporting insights about harmony and theory, advancement of guitar skills and technique, and innovative creative work. The article outlines NOJ\u27s core concepts and teaching principles, describes NOJ\u27s pedagogical sequence of topics and supporting exercises and other activities, and provides background on the method\u27s origin and evolution. This description reflects the author\u27s perspective as the developer of NOJ, based on two decades of experience in teaching and refining the method, including the design of two levels of NOJ-based courses, incorporated into the songwriting curriculum at Berklee College of Music