Artificial Intelligence and Approaches to Music Education

sentimental In ordinateigence and Approaches to harmony learningAbstractThe end of this typography is to brush up the principal getes to unison Education with a management on insubstantial Intelligence (AI). Music is a domain which reads creativity, problem-seeking and problem-solving think ofively, from both learner and teacher, and thence is a ch every last(predicate)enging domain in Artificial Intelligence. It is argued that remedial respect commensurate omnib victimisation- schemas argon deficient for teaching a subject that requires unrestricted intellection. tralatitious classroom methods atomic number 18 sometimes favoured beca lend whizself tutors keep concentre on mortal differences and enhance creativity and motivation.However, it enkindle excessively be argued that AI is a mechanism which enables those without conventional medicational skills to wee-wee medicine. Almost the only goal that applies to music study in frequent is compose someth ing interesting (Levitt, 1985). This paper leave behind review diametrical onslaughtes to AI in Music Education. Approaches guessed erect be capable Tutoring agreements in Music AI base Music Tools highly interactive interfaces that employ AI theories.1. IntroductionThis paper forget review some of the undertakees to victimisation Artificial Intelligence in Music Education. This cross field is of high interdisciplinary and involves contri onlyions from the fields of knowledge, music, artificial word (AI), the psychology of music, cognitive psychology, human com siteer interaction, school of thought, com imputeer science and galore(postnominal) others. AI in genteelness itself is a very broad field, dating from around 1970 (Carbonell, 1970) and has its have theories, methodologies and technologies. For brevity, we testament abbreviate Artificial Intelligence in Education to AI-ED, following a standard convention.DefinitionsThe scope of AI in Education (AI-ED) is not decisive, so it will be efficacious to consider some definitions. A common definition is whatsoever application of AI techniques or methodologies to educational ashess. Other definitions which revolve around more narrowly atomic number 18, for movement every computer-based learning system which has some degree of autonomous decision-making with respect to some aspect of its interaction with its drug lend oneselfrs (Holland, 1995). This definition suggests the requirement that AI techniques motive with the user at the point of interaction.This might be in recounting to best teaching antenna, the subject being taught or every misconceptions or gaps in the scholars knowledge. However, AI-ED in a wider background is sometimes defined as the use AI methodologies and AI agencys of thinking apply to discovering insights and methods for use in education, whether AI architectural plans ar involved at the point of delivery or not (Naughton, 1986). In practice, these con trasting set outes mutation a continuum.Music An open-ended domainA useful bankers bill in AI-ED is between formalised domains and the more open-ended domains (domain instrument subject atomic number 18a to be taught). In relation to domains much(prenominal) as math and Newtonian dynamics at that place ar straighten out targets, cryst entirelyise answers and a reasonable clear and concise complex body part to follow for success. Whereas in open-ended domains such as music composition, there argon in public, no clear goals, no driven criteria to follow and no cover answers.The focus is based upon, as mentioned earlier, Compose something interesting (Levitt, 1985). Rittel and Webber (1984) describe this finicky problem in domains as wicked problems. In such domains there potnot be a definitive formulation for the problem or the answer. irreclaimable domains such as music composition require learners to not estimable solve problems but in each case seek problems ( induce, 1994). The term problem seeking is employ in a number of disciplines such as sensual behaviour (Menzel, 1991). Cook (1994) imported the term into AI in Education in detail reference to the sense of philosopher Lipman (1991). In this sense Cook (1994) refers to the term problem seeking as followsProblems ar treated as ill-defined and open-ended on that point is a continual intertwining of problem specification and solutionCriteria for completion is very confineContext greatly affects the recital of the problemProblems are al delegacys open re-interpretation and re-conceptualisationIn relation to communicative performing arts and music composition there is no goal or problem to be solved. The learner mustiness find or create goals and problems which then may need to be revisald, modified and rejected where best suited to his/her taste.2. Computer-Aided InstructionIt is worth considering briefly the music education programs that negligibly use AI as a background to AI accessiones in education. Historically, computers utilise in music, and most other subjects, were associated with the theory of learning behaviourism. These particular systems (branching teaching programs) stepped through the following algorithm (OShea and Holland, 1983),Present a honk to the assimilator i.e.Present the student with pre-stored material (textual or audio visual) bespeak a response from the studentCompare the response with pre-stored alternative responses shit any(prenominal) pre-stored comment associated with the responseLook up the next build to present on the grounding of the responseAn example of this kind of system was the GUIDO ear- cultivation system (Hofstetter, 1981). Branching teaching programs tend to respond to the user in a manner that has more or less been explicitly pre-planned by the author. thitherfore, this tends to limit the approach to a simplex treatment. multimedia system and HypermediaMultimedia and hypermedia has had a great impact o n music education and transformed music education software programs, giving a different emphasis from the earlier behaviourist programs. Recent educational music programs such as Seventh Heaven, Ear Trainer, Interval and Listen aim to countenance practice in recognising or reproducing intervals, reconciles or melodies. MacGAMUT is a classroom perplexing program that dictates exercises and provides a detailed marking scheme.Other programs such as MiBAC Music Lessons, Perceive and Practica Musica offer a comprehensive ear training program including scales, durations, modes and tuning. See Yavlow (1982) for information on the aforementioned programs. Since the domain is relatively clear-cut and non-problematic, ear training and music theory are favorite methods in non-AI music education programs. There are many useful melodic theater computer alikels applicable to education such as music editors, charmrs, computer-aided composition tools, multimedia reference tools on CD-ROM Master cyphers and much more.3. heavy Tutoring formations for Music A Classical ApproachThe history of AI in education force out be divided into deuce currents, the classical period (1970 1987) and the modern period (1987 to present day). In the classical period, the tether role tralatitious imitate of an Intelligent Tutoring System (ITS) was the most common and prestigious idea. This mould was sometimes extended to a four component model. later on 1987, ideas had shifted to finding alternative ways around the traditional model. However, this was limited out-of-pocket to look into available at those times, and the traditional model remains powerful and is still employ to the present day.Each of the three components of the traditional model give the axe be considered a separate keen system. The traditional ITS model (Sleeman and Brow, 1982) consists of three AI components, each an skillful in its have got area. The first component, the domain model, is an expert in the subject being taught. So in the case of a vocal tutor, the domain expert itself would be able to perform vocal tasks. This requirement is essential if the system is to be able to answer unforeseen questions in relation to the task in hand.The second component is the student model. Its purpose is to build a model of the students knowledge, capabilities and attitudes. This will allow the system to vary its approach in accordance to the individual student. In essence, the student model can be viewed as a checklist of skills. This is sometimes modelled as an overlay i.e. a tick list of the elements held in the domain. Sophisticated models may view it as a deliberately distorted element or a improper expert system. These errors are intended to mirror a students misconceptions.A fair diagnosis of a students knowledge, skills, capabilities and beliefs is much a hard problem in AI. One partial way around the diagnosis problem would be to ask the student close their capabilities, b eliefs, previous experience and so on. A more stringent approach is to come in the student tasks specifically knowing to analyse their skills. The results can then be used to ready the student model.The trinity component of the traditional ITS model is the teaching model. Typically, this may consist of teaching strategies such as Socratic tutoring, coaching and teaching by analogy (Elsom-Cook, 1990), to simply allowing the student to explore available materials unhindered, with or without the guidance of a human teacher. The poop component is an interactive user interface for the tasks mentioned, if it is used. Note that not all Intelligent Tutoring Systems consist of all three components. It is common to have a of import focus on one maybe twain components, and omit, or greatly simplify the others.In particular, most ITSs in music focus on the expert or student model. Irrespective of the emphasis, ITS models require an explicit, formalisable knowledge of the task. However, m any skills in music correspond to wicked problems and are resistant to explicit formalisation. This narrows the number of areas ITS models can be applied to in music education. An example area is Harmonisation. It is one of the few musical topics for which relatively detailed, determines of thumb can be found in a textbook. barely even here, the traditional ITS model may not be effective.There are two systems from the classical ITS period, which are good examples of the potential and limitations of the ITS approach in music, Vivace and Macvoice.3.1 Vivace An expert systemVivace is a four-part choral writing system, created by doubting Thomas (1985). Vivace is not an ITS model in itself, yet has formed the basis of one. It takes an eighteenth coke chorale melody and writes a bass line and two inner voices that retard the melody. It uses text from books, abstracted from the practice of past composers, to employ rules and guidelines for harmonisation. These rules can be categorized into four types firm requirements, preferences, firm prohibitions, less firm prohibitions.There are three specific problems which can be identified for any human or machine when trying to harmonise on the basis of the rules. The first problem is hence common in beginners classes, to satisfy all the formal rules and give rise a composition which is correct but aesthetically unsatisfactory. The second problem is that most of the guidelines are prohibitions rather than positive suggestions. Milton Babbit ob serves that the rulesare not intended to tell you what to do, but what not to do (Pierce, 1983).In other words, if we view harmonisation as a typical AI generate and test problem, the rules constitute weak help in the interrogatory phase, but little help in well focused generation. The ternion problem is that it is quite impossible to satisfy all of the preferences at any one adjudgen time. Some preference rules may have to be broken. A clear order of importance of preference rules is not delegate by traditional descriptions in fact, it is not at all clear that any fixed order would bring sense.However, it is possible to write a rule-based system that implements text book rules. In principle, a traditional ITS system can use these rules to criticise students work and serve as a model of the expertise they are supposed to acquire. In relation to the limits aforementioned, how useful or effective would such a tutor be? Thomas used the tutor to illuminate the limitations of the theory. By using Vivace, Thomas was able to establish that text book rules are an inadequate characterisation when performing such a task at expert level.Thomas ascertained using only conventional rules about straddle and movement the tenors voice would most certainly move to the top of its swan and stay there. Thomas suggested that there must be a set of missing rules and metra-rules to fill theses gaps. He used a Vivace data-based tool to establish this gap. In each try out Th omas had to use his intuition to decide upon whether the results were musically viable or not. Thomas discovered that many of the traditional rules were overstated or needed redefining. He in like manner unveiled natural guideline and was able to understand the task at a more strategic level. With the hangance f her human pupils, Thomas formulated a number of heuristics for what to do rather than not what to do.Experiments with Vivace enabled Thomas to realise the need to find human pupils apprised of high level phase social structure prior to detailed play writing. As a result of her experiments, Thomas was able to use her new knowledge about the task, as a result of teaching her expert system, and write a new teaching text book based on her findings. Part of this knowledge was used in a simple commercial ITS, which criticises students voice-leading (MacVoice).3.2 MacVoiceMacVoice criticises voice-leading aspects of four part harmonisation. It is a macintosh program based o n the expert system Vivace. The MacVoice too includes a music editor as part of its interface. MacVoice makes it possible to stimulus any set, any harmonise at a time or a voice at a time, or notes in any confounded fashion. As soon as a note is placed on the stave, it will display its guess as to the snuff it of the corresponding chord in the form of an annotated Roman numerical.Three are two grand limitations of this system as follows firstly, all chords must form Homophonic blocks (all notes must be of the same duration) and secondly, the writing must be in a single key. There is one other menu function, called voice-leading.This particular function inspects the harmonisation in line with a set of base rules for voice-leading, indicating any errors. MacVoice is quite flexible to use.MacVoice has been used virtual(a)ly at Carnegie Mellon University. MacVoice does not give positive strategic advice. It only points out errors. It does not address the expertness or any othe r benefits of the chord sequences involved. Further research on this topic may include a visual display of what the voice-leading constraints are, or the possible preferred outcomes.3.3 LassoLasso was formalised by lux (1725). It is an intelligent tutoring system designed for the 16th century counterpart and is limited to two voices. Newcombs approach focuses on intending to provide simple and pursuant(predicate) guidelines to help students know what is required to pass exams. The process of codification of the inevitable knowledge goes beyond that of text book rules and guidance. Like Thomas, Newcomb was aware of this, however, approached it using a probabilistic manner, analysing scores to find out such facts as the allowable ratio of skip to non-skip cantabile intervals and how many eighth note passages can be expected to be found in a piece of a given length (Newcomb, 1985).Also, the knowledge used for criticising students work is being coded as branch procedural code. There are as well as unvarying canned error messages, help messages and congratulatory messages. This will assist students, offering some form of motivation. Lasso is a very heroic system. It has a quality musical editor, tackles complex musical paradigm and has been used in real teaching contexts. However, there are some ingrained problems. The rules are at a very low level, and there are a high number of them. There is a system rule which prevents over one hundred comments being made about any one given attempt to complete an exercise. For example, typical remarks made by Lasso includeA melodic interval of a third is followed by stepwise motion in the same direction.Accented canton passing note? The dissonant quarter note is not preceded by a descending step. (Newcomb, 1985).The quantity of relevant text required to put in help context of myriad low-level criticisms could slowly suppress students. Students complained that it was so difficult to meet Lassos demands that they were forced to revise the same task repeatedly. A solution to this problem would be to represent general principles to govern the low-level rules. exploitation such codified principles will reduce the number of comments required to relevant text and generalise observations.3.4 concluding remarks on Intelligent Tutoring Systems A Classical ApproachThe traditional Intelligent Tutoring System approach assumes an objectivist approach to knowledge. Such systems depend on the given there is a well-defined body of knowledge to be taught and can be put into precise concepts and relationships. This works with four-part harmonisation and 16th century counterparts. However, in a more open-ended context, an objectivist approach can be very limited. In domains which are artificially limited, teaching of rules drawn from practical experience tends not be a very good approach.Using verbal definitions to teach a musical concept is limited and does not compare to the knowledge required to identify th e true meaning of these definitions to be an experienced musician. It is all very well to define a chord, a dominant eighth in terms of its interval pattern and provide general rules but to an experienced musician the meaning of a chord or a dominant eight is much more depending on the context. Being able to intelligently manipulate structures is far more essential than to tho being able to understand and obey a set of rules, which an experienced musician will be capable of doing so. Rather than unless a set of explanations, a student needs a organize set of experiences making them more aware of musical structures, being able to manipulate them intelligently and most importantly, more capable of formulating sensible musical goals to pursue.4. Open-ended Microworlds The logo PhilosophyA contrasted idea from the classical approach of AI in education, which is practiced as influential as the whim of an ITS is the logotype approach (Papert, 1980). The logo philosophy has particul ar attractions to open-ended domains such as music. It focuses its approach on the idea of an educational microworld. An educational microworld is an open-ended environment for learning. Therefore, there are no specific built-in lessons. The logotype approach in associated microworlds does not need to involve much, or indeed any AI at point of delivery.However, their designs tend to be strongly influenced by AI methodologies and tools. A simple version of AI scheduling language is used to build microworlds. Students are encouraged to write or modify programs as a means of exploring the domain. logotype doubles as the name of scheduling language based on Lisp, used for just this purpose. There are three distinct elements in the Logo approach Logo (and alike languages) as a programming tool Logo as a vehicle for expressing various AI theories for educational purposes and Logo as an educational philosophy.Firstly, we will briefly explore Logo as an educational philosophy. In its ea rly work, Logo was mainly used for mathematics learning, poetry and music. One of the versions encouraged children to produce new melodies by rearranging and modifying melodic phrases. The learning philosophy was aimed to enable children to have a better savvy of the concept by making them envision or pre-hear a result. Thus, alter them to work out how to achieve it, and realise the reason behind obtaining an surprising result. This learning philosophy was derived from a number of sources, including the psychologist Piagets notions of how children construct their own knowledge through play.The Logo approach in relation to microworlds can be somewhat complex. Students are sometimes provided with a simplified version of an AI model in some problem domains. For example, in the case of music composition, fragments of illustrative material can be generated using rich grammars as models of particular composition techniques. The supplied programs can be used by students to explore, cri ticise, and refine their own (or someone elses) model of process.Notice that none of the three components in the ITS model are required in the Logo approach. In practice, students need some form of guidance from teachers in order to make use of their full potential using Logo systems. If there is no guidance from a teacher the students risks only learning a technique without appreciating the wider possibilities and understanding the true meaning of being an experienced musician. The educational philosophy associated with Logo has been applied to a number of systems in music at different levels and in different ways, as mentioned below.4.1 Music Logo System Bambergers SystemJeanne Bambergers Music Logo System (1986, 1991) can be used to work with sound cards or synthesisers. It uses programming elements called functions to structure and control musical sounds. Music Logos central data structure is a list of integers representing sequences of durations and agitatees, which can be sto red separately. These can be manipulated separately before being played by a synthesiser. So for example, to play A above middle C for 30 beats, then middle C for 20 beats, then G for 20 beats , the following expression might be used.Play a c g 30 20 20Programming constructs such as repeat can easily be understood by beginners to do musical work. Using arithmetical and list manipulation functions, note and duration list can be manipulated separately. Features such as recursion and random number generators can be used to build complex musical structures. Common musical operations are provided (list manipulation functions).For example, one function takes a duration a pitch list and generates a number of repetitions of the phrase shifted at each repetition by a constant pitch ontogenesis, creating a simple sequence (in a musical sense of the term). Bambergers Music Logo System also provides other musical functions, such as retrograde (reverses a pitchlist), invert (processes a pitch list to the complimentary set deep down an octave), and fill (makes a list of all intermediate pitches between two specified pitches).To try and guess a musical outcome, manipulate lists and procedures or conversely iteratively manipulating lists of representations to try to reproduce something previously imagined, Bamberger suggests many simple exercises. These techniques, in many ways, are a reflection of educational techniques suggested by Laurillard (1993) for general use in higher education. There are two particular classes of phenomena suggested by Bamberger, which emphasises the importance of shock and learning experiences.Firstly, perceptions of phrase boundaries occur in melodic and rhythmic fragments dependent upon small manipulations of the duration list. Secondly, there is an maverick difference between degree of change in the data structure and the degree of the perceived change produced. In priniciple, the Logo system allows students to focus on manipulating any kin d of musical structuring technique. However, in practice the focus tends to be on simple, small scale structures such as motives, and their transformation.4.2 A series of microworlds LocoPeter Desain and Henkjan Honing developed a series of microworlds and tools applying the Logo philosophy. The first series was the chapped (Desain and Honing, 1986, 1992). The second was POCO (Honing, 1990), followed by Expresso (Honing, 1992) and LOCO-Sonnet (Deasin and Honing, 1996). All of these microworlds guardedly reflect the thought behind AI methodologies and how they can be applied to music education.LOCO is similar to Bambergs Logo, in the sense it also focuses on music composition. The central component is a set of tools for representing sequences of musical events, which can be interfaced with any output device or instrument. It is also flexible enough to take input from practically any composition system.Microworlds provided each offer tools for useful style- freelance composition tec hniques, particularly stochastic processes and context easy music grammars. Two musical objects provided essentially are just rests and notes. LOCOs time structuring mechanism is simple and elegant. There two relations, Parallel and successive used to combine arbitrary musical objects. Sequential is a function which causes musical objects in an argument list to be played one later another, whereas, Parallel is a function that causes arguments to be played simultaneously.It is quite simple to nest a parallel structure within a sequential structure, and vice versa. Sequential and Parallel objects are treated as data which can be computed and manipulated before they are played. The result- arbitrary time structuring can be applied with much flexibility. As mentioned earlier, LOCO provides a base for composing using stochastic processes and free grammar context. Various effects can be produced, depending on how variables are defined, includingA random election among its possible va luesA choice leaden by a probability distributionA random choice in which previous values cannot recur until all other values have been chosenSelection of a value in a fixed circular orderThe above are easily put together using composition (in a mathematical sense) of functions. For example, the value of an increment could be specified as a stochastic variable. This can produce a variable that performs a Brownian random walk. Brownian variables can be used, for example, as arguments in commands to instruments within a time-structured fabric. These techniques can be used to construct concise, easy to read programs for transition nets and other stochastic processes. Using general programming language in each case, the operation of a program can be modified. See Ames (1989) for more information in the integrative uses of Markov chains.The primary design goals of LOCO include ease of use by non-programmers to experts. A more recent version of LOCO, LOCO-Sonnet mirrors LOCO but also in cludes a graphical front end. Sonnet is a domain independent data flow language originally designed for adding sound to user interfaces drawn from Jamesons (1992) Sonnet. It is designed for use by both novices and experts alike. LOCO has been used in workshops for novices and professionals and even has courseware available.4.3 Concluding comments on the Logo approachThe Logo approach is known to be associated with constructivism. Constructivism, in the aspect of knowledge and learning, suggests that even in the cases where objectively true knowledge, exists simply presenting it to a student limits the effects of their learning. It based on the assumption that learning arises from learners being interactive with the world, which will force them to construct their own knowledge.The result of this knowledge will vary between individuals creating unique ideas and outcomes. This fits in very well with open-ended domains such as music where the basis of knowledge is learning how to create your own masterpiece.Unlike classical Intelligent Tutoring Systems, Logo requires intensive body forth from a human teacher. This can be viewed as both flunk and strength of the program. Intelligent Tutoring Systems and the Logo approach were both influential ideas of AI in education in the early years. As both strengths and limitations were notable over the years, combining characteristics of the two became a prime focus of research which led to Interactive Learning Environments (ILE). We will talk about this after a brief discussion on AI-based tools.5. Applications in Education focus on AI-based toolsThere are a number of application tools employing AI but its purpose is not primarily educational. However, it is useful to consider some of these systems as they nevertheless have clear educational applications. There are quite a few programming languages based on AI languages such as LISP and CLOS that have a relatively similar technical aspect to that of the Music Logo systems described earlier. However, the philosophy of use may be quite different. The commercial system Symbolic Composer (for macintosh and Atari) is one example of this difference.It has a vast library of functions, including neural nets facilities, used for processing, generating and transforming musical data and processes, commonly built on Lisp. The system is primarily aimed at composers and researchers. Another culture which offers an educational paradigm with many relate to AI culture is the Smalltalk culture. An example of such a system is Pachets (1994) MusES environment, implemented in Smalltalk 80. It is aimed at experimenting with knowledge representation techniques in refreshing music.MuSES includes systems for harmonisation, analysis and improvisation. Finally, an example of a commercial program is Band in a Box (Binary Designs, 1996). It takes a chord sequence as input and at output can play an accompaniment based on the chord in a wide variety of styles. At one moment in time this would have required AI techniques but in todays era it is a conventional method.6. back up learning with Computational Models of Creativity6.1 A cognitive support material constraint-based model of creativityI noticed that the drawing teacher didnt tell people much.Instead, he tried to inspire us to experiment with new approaches. I thought of how we teach physics we have so many techniques-so many mathematical methods that we never stop telling the students how to do things. On the other hand, the drawing teacher is afraid to teach you anything.If your lines are very heavy, the teacher cant say your lines are too heavy because some artist has figured out a way of making great pictures using heavy lines. The teacher doesnt hope to push you in some particular direction. So the drawing teacher has this problem of communicating how to draw by osmosis and not by instruction, while the physics teacher has the problem of always teaching techniques, rather than odour of how to go about solving physical problemFeynman (1986)John and I.were quite happy to nick things off people, becauseyou start off with the nicked piece and it gets into a the songand when youve put it all togetherof course it does make something originalPaul McCartney quoted in (Moore, 1992)There are limitations present in both traditional AI approaches in education mentioned earlier (ITS and Logo). ITSs don not work very well in problem-seeking domains and Logo type approaches require support from a human teacher in order to be effective. One way of investigating these problems has been addressed by MC (Holland, 1989, 1991 Holland and Elsom-Cook, 1990). MC is an acronym for both Meta Constraints and Master of Ceremonies, which is a general framework for interactive learning environments in open-ended domains. We will focus on the domain model rather than the teaching model.The current version is designed at teaching ab initio students to compose tonal chord sequences, with partic