ROBOTABA GUITAR TABLATURE TRANSCRIPTION FRAMEWORK Gregory Burlet and Ichiro Fujinaga Centre for Interdisciplinary Research in Music Media and Technology McGill University, Montreal,´ Quebec,´ Canada [email protected], [email protected] ABSTRACT This paper presents Robotaba, a web-based guitar tabla- ture transcription framework. The framework facilitates the creation of web applications in which polyphonic tran- scription and guitar tablature arrangement algorithms can be embedded. Such a web application is implemented, and Figure 1. Tablature notation depicting six different string consists of an existing polyphonic transcription algorithm and fret combinations to perform the note E4 on a 24-fret and a new guitar tablature arrangement algorithm. The re- guitar in standard tuning. sult is a unified system that is capable of transcribing gui- tar tablature from a digital audio recording and display- transcription process, the guitar can produce the same note ing the resulting tablature in the web browser. Addition- in several ways. For example, there exists six string and ally, two ground-truth datasets for polyphonic transcrip- fret combinations that can produce the note E4 on a 24-fret tion and guitar tablature arrangement are compiled from guitar in standard tuning (Figure 1). manual transcriptions gathered from the tablature website While several polyphonic transcription and guitar tab- ultimate-guitar.com. The implemented transcription lature arrangement algorithms have been proposed in the web application is evaluated on the compiled ground-truth literature, no frameworks have been developed to facilitate datasets using several metrics. the combination of these algorithms to produce an auto- matic guitar tablature transcription system. Moreover, af- 1. INTRODUCTION ter a new polyphonic transcription or guitar tablature ar- rangement algorithm is developed, the code has no imme- Tablature has become the primary form of communication diately available vessel to be used by music researchers and between guitarists on the Internet. Guitar tablature is a mu- the large community of guitarists on the Internet. sic notation system with a six-line staff that represents the A web-based guitar tablature transcription framework, strings on a guitar. A numeric entry on a line represents entitled Robotaba, has been designed and implemented to the fret to depress on a particular string (Figure 1). facilitate the creation of guitar tablature transcription web Manually transcribing guitar tablature from an audio applications in which polyphonic transcription and guitar recording is a difficult and laborious task, even for expe- tablature arrangement algorithms can be embedded. An rienced guitarists. In response to the time-consuming pro- existing polyphonic transcription algorithm and a new gui- cess of manual transcription, automatic music transcrip- tar tablature arrangement algorithm is implemented; these tion systems aim to extract a symbolic music score from algorithms are embedded in a transcription web application an acoustical signal [5]. Specifically, automatic guitar tab- using the Robotaba framework. Two ground-truth datasets lature transcription systems transform a digital guitar sig- have been compiled to evaluate the performance of the im- nal into tablature notation. The task of automatic guitar plemented guitar tablature transcription system. tablature transcription can be decomposed into two sub- The structure of this paper is as follows: The next sec- problems: polyphonic transcription and guitar tablature ar- tion provides an overview of polyphonic transcription and rangement. Polyphonic transcription algorithms extract the guitar tablature arrangement algorithms. Section 3 presents pitch, onset time, and duration of notes occurring in an au- the design of Robotaba, followed by a description of the dio recording. Guitar tablature arrangement algorithms as- implemented transcription web application in Section 4. sign a string and fret combination to each note occurring Section 5 presents the compiled ground-truth datasets and in an input music score. Adding more ambiguity to the their use in the evaluation of the implemented polyphonic transcription and guitar tablature arrangement algorithms. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are 2. LITERATURE REVIEW not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Though the transcription of monophonic musical passages c 2013 International Society for Music Information Retrieval. is considered a solved problem [5], the transcription of polyphonic music is still an open problem [1]. Several techniques have been proposed to accomplish the task of polyphonic transcription, including human audition mod- elling [12]; salience methods, which apply transformations to the input audio signal in order to emphasize the underly- ing fundamental frequencies [17]; iterative and joint funda- mental frequency estimation algorithms followed by note Figure 2. Modular architecture of the Robotaba guitar tab- tracking algorithms [2]; and a variety of machine learning lature transcription framework. methods such as non-negative matrix factorization [14], support vector machines [8], neural networks [6], and hid- den Markov models [10]. file containing the estimates of note events occurring in the Several algorithms have also been proposed to generate input audio file. The polyphonic transcription module op- tablature from a symbolic music score according to crite- tionally postprocesses the output symbolic music file by ria that minimizes the performance difficulty of the tab- limiting the number of simultaneous notes to six, and dis- lature arrangement. Heijink and Meulenbroek [4] estab- carding or transposing estimated notes that are outside of lished that guitarists have a disposition toward instrument the range of a specific guitar. Properties of the guitar (num- fingering positions that are biomechanically easy to per- ber of frets, tuning, and capo position) 1 and postprocess- form. Specifically, guitarists favour hand positions near ing options are specified by the user of the web application. the head of the guitar and avoid composing arrangements that require extensive hand repositioning and large finger 3.2 Guitar Tablature Arrangement Module spans. Shortest-path graph search algorithms [13], con- straint satisfaction algorithms [9], neural networks [15], The guitar tablature arrangement module accepts an MEI and genetic algorithms [16] have been used to search for file as input, which is mandatorily preprocessed in an iden- tablature arrangements with minimal performance difficulty. tical manner as the postprocessing step of the polyphonic transcription module described in the previous section. The preprocessed MEI file is subsequently passed to the guitar 3. FRAMEWORK DESIGN tablature arrangement algorithm embedded in the module, The Robotaba transcription framework is composed of three which is responsible for assigning a guitar string and fret modules: a polyphonic transcription module, a guitar tab- combination to each note occurring in the MEI file. lature arrangement module, and a guitar tablature engrav- ing module (Figure 2). 3.3 Guitar Tablature Engraving Module Three benefits arise from this modular design: First, each module can be used independently or together. Used The guitar tablature engraving module is responsible for independently, an input file is sent directly to a module for parsing an MEI file containing a sequence of note events processing, which returns a result instead of passing the that have each been assigned a guitar string and fret com- output to the next module in the workflow. Using each bination and displaying the encoded tablature in the web module in sequence, guitar tablature can be generated from browser. Robotaba uses the digital guitar tablature engrav- 2 an input audio file and displayed in the web browser. Sec- ing library AlphaTab to render tablature symbols on the ond, the modular design facilitates algorithm interchange- hypertext markup language (HTML) canvas element. Al- ability. Assuming an algorithm produces valid output, it phaTab parses drawing scripts called AlphaTex, in which can be inserted into a module without disturbing the func- structured keywords inform the rendering engine about the tionality of surrounding modules. As a result, the tran- contents of the tablature and how it should be displayed. scription framework can accommodate new state-of-the-art When an MEI file is passed to the tablature engraving mod- polyphonic transcription or guitar tablature arrangement ule, the contents of the file are converted to an AlphaTex algorithms without substantial changes to the web appli- drawing script to be rendered by AlphaTab. A rendered cation. Third, the use of a single symbolic music file for- tablature score can be seen in Figure 1. mat for data interchange between modules promotes poly- phonic transcription and tablature arrangement algorithms 3.4 Framework Implementation to adhere to a common interface. Robotaba uses the 2012 3 release of the music encoding initiative (MEI): an extensi- Robotaba is implemented using Django, a Python web ble markup language (XML) file format that encodes sym- framework that facilitates rapid development of database- bolic music notation in a hierarchical fashion [11]. driven web applications by automatically translating