Computer-Aided Algorithmic Composition in Popular Music Brian Tuohy

Digital Media and Arts Research Centre University of Limerick

Castletroy, Limerick, Ireland 0743518@studentmail.ul.ie

Abstract Computer-aided algorithmic composition (CAAC) has been met by much criticism from the musical establishment. With few traditional composers willing to accept the methods of CAAC as valid, respectable approaches to musical composition, it is unclear whether or not there is a place for computer-aided algorithmic composers within a mainstream musical practise. This paper seeks to examine the issues that surround the field of CAAC, and, in particular, the work of one of its primary practitioners, David Cope.

Keywords: Algorithmic Composition, CAAC, Computer Music, David Cope, Generative Music.

1. Introduction The purpose of this research paper is to investigate the use of CAAC in creating popular music. The issue of using computer-based algorithms to make musical decisions while composing a piece can be held in great contempt among some traditional composers. The question of indolence can be raised as an argument against the choice to employ the assistance of a computer in musical navigation.

This paper raises and attempts to answer the following question: Can CAAC methods be accepted into the current mainstream as a compositional tool? In order to answer this question, several issues surrounding the field of CAAC must be examined. These issues include:

• Disapproval of non-traditional composition methods

• Challenges of integrating proven methods beyond the realm of academia

• Limitations of specific systems

There is also a question raised as to who should receive the credit for such compositions. Is it the composer upon whom the work is based, the computer that carried out the calculations, the programmer of the software, or the user? There are several different areas involved in studying CAAC. Some of these areas are concerned with the concepts of algorithmic composition and its background,

others include the application of these methods to popular music and the success of the results, while more relate to the objections that some composers have to the idea of automated composition, and how it might be at odds with the ethos of creative art. To reflect these areas, this paper has been broken up into three main sections:

• Background to CAAC

• Practical Applications of CAAC

• Objection/Support for CAAC

Throughout the following chapters, we will first investigate the history of algorithmic composition, attempting to determine what, if anything, differentiates it from other approaches to musical composition and creativity. We will then look at how algorithmic composition has been implemented with computers, and what applications it has found beyond the theoretical realms of academia. Finally, we will discuss the debate that exists between proponents and opposers of CAAC, and attempt to decipher whether or not CAAC could ever be accepted as a valid method for creating popular music.

2. Algorithmic Composition An algorithm is defined as “a set of rules for solving a problem in a finite number of ways”[1]. Although the term is often associated with computers, algorithms can also take many other forms, such as written instructions or verbally dictated rules. Any form of structured music, then, can surely be seen as containing algorithms – rules which suggest the best methods for composition, structure etc.

2.1 Algorithmic composition throughout history Algorithmic composition is said to extend back thousands of years [2]. Some of the first musical instruments were capable of creating music that would act on set rules, but would depend on an input so variable that the sound could differ greatly with every performance. One such example is a wind-chime [2]. The designer of such an instrument sets certain things in concrete – the possible pitches, amplitudes, etc. that the instrument might achieve. Acting on the instrument, however, is an entirely unpredictable source, not affected by any concepts of music or structure. Wholly different orchestrations are possible with each gust

of wind. The wind introduces randomness into the musical output of the chimes, in much the same way as computers can with algorithmic composition today [3].

Musical influence and plagiarism are at the center of many debates about algorithmic composition. It could be argued that all music is simply the product of the sounds by which it is preceded [4]. Early music can be traced back to man’s association with nature and the elements. The first rhythms are said to have been inspired by the sounds of the human heart beating and our feet first creating sonic patterns, as we discover the possibilities of our hind legs. Similarly, early melody can be attributed to the sounds of birds and the musical calls of other animals such as elephants. Music, then, is rooted in association and emulation – we could not create sound without first being inspired and influenced [3].

In terms of Western music, algorithmic systems to aid the creation of musical scores existed long before the invention of computers [5]. One such system was Musikalisches Wurfelspiel (musical dice game). This system was largely used in the 18th century by composers such as Haydn and Mozart. The concept of the system involved throwing a dice in order to randomly select a measure from a collection of several hundred small phrases. These measures would then be pieced together to create a composition in a similar manner to some of the techniques used in computer-generated composition today [3]. 2.2 Early implementation of computers in algorithmic composition There was little change in concept when algorithmic systems transferred from pen and paper to computer around the middle of the twentieth century. Gottfried Koenig, Lejaren Hiller and Iannis Xenakis all helped pioneer early CAAC [6]. Of particular importance to CAAC were stochastic methods of composition, such as those used by Xenakis. These processes involved feeding a table of probability statistics into a computer and then generating a random number from within this table. This number could represent pitch, amplitude, duration, timbre or a number of other musical characteristics. The number would be documented as part of a score, and another number would be generated based on the probabilities associated with the current number within the statistics table.

2.3 Modern developments The same stochastic methods used by Xenakis are still widely used today [7]. Markov chains are often used to create melodies from a probability matrix. These probabilities allocate a different weighting to each possibility of pitch, amplitude, etc. The use of higher order Markov chains lends itself to the generation of results with a much stronger sense of structure than the simple “aimless wandering” of a first order system [6]. Higher orders mean that the relationships examined go beyond that of the

current and previous note, but rather depend on a series of notes, leading up to the current request. This leads to a much denser table of probabilities, with the tendency to break off into seemingly organised sections and patterns.

The ebb and flow tendency of Markov chain-based algorithmic music – in its jumps between sections – serves to question the ability of such systems to contribute to an overall form or direction within a piece of music [8]. Without an overall direction, much of the work of algorithmic systems can remain static and incomplete [9]. The study of form in music is largely based in the field of psychology and, because of this, it has been suggested that computers may not understand the overall form of a piece without the input of human guidance [8].

If a computer is taught to recognise melodic patterns within a piece, then it can use this analysis to further inform the composition of future pieces. The ability to recall such recurring patterns allows for certain properties of a score to be “earmarked” for later use [10]. This is the approach taken by David Cope with the use of Experiments in Musical Intelligence (Emmy) [1]. Cope created a database of classical works from Bach, Mozart, Vivaldi and Rachmaninoff, among others. From these works, it was possible to identify certain patterns, motifs and techniques, which could then be emulated to create works of a similar style. This analysis of the “composition of a composition” has been referred to as the ‘metalevel’ of music – the next natural abstraction of music beyond the written score [11]. Such analysis can provide invaluable information regarding the nature of composition and how we perceive what is considered to be beauty within music.

3. Beyond the Ivory Tower – Practical Applications of CAAC Much of the work that surrounds computer music can often remain bound to an academic environment and fail to reach a wider audience. This section investigates some examples of work that has attempted to implement practical applications of CAAC. Some of the uses suggested include the composition of popular music, automatic composition of incidental music for film and TV, and compositional training for young musicians.

3.1 Classical Composition David Cope’s recent work has ventured far beyond the realms of mere tribute to classical composers. This comes as a result of the creation of Emily Howell, a new software that endeavours to achieve what Cope had first intended in 1980 with EMI – to develop a compositional tool that could interpret his style and assist him in conquering writer’s block, with the reconfiguration of his own ideas [12]. This is achieved by constant interaction with the system, in the form of conversations. Cope will ask Emily

a question regarding the direction of a piece of music, she will respond with a suggestion – based on a database of Cope’s style and that of several composers by whom he is influenced. Cope will either accept or reject this suggestion, thus leading the system to grow further informed about the composer’s style and preference [3]. The use of Emily Howell’s inherent feedback loop, which bases decisions and further development on the input and critique of the composer, suggests the possibility of self-aware computer programs [12]. Cope describes such artificial creativity as being the product of inductive association, recombinance, pattern-matching, and learning from allusions and influence [13].

The software is derived from the composer’s rules, but, in order for it to produce anything indicative of personality or life, the program must also be capable of breaking these rules. This is a common belief for much of CAAC, and applies equally to all genres. It is the very lack of personality in computers that allows them to introduce notions that may seem inconceivable to a human composer, and to “reassemble old elements in new ways, without the hang-ups or preconceptions of humanity” [3].

This is a sentiment echoed by Brian Eno, who has stated that he aspires to create music that is not only interesting because of its principles, but also because of its musical effect. To achieve such results, one must allow for the system to step outside of the prescribed rules. “I want the ideas to be seductive, and the result to be seductive” [14].

The work put forward in Justin Fincher’s thesis attempts to automatically create new pieces of music, based on the analysis of the style of previous works [16]. This is not dissimilar to some of the work that David Cope achieved with Emmy, although the author does admit that the system deals with much less complex scores. It is noted that the computer lacks the creativity of a human composer and does not possess the intelligence to create its own style, but instead simply mimics the style of composers it has analysed. This has informed the suggestion that such programs could be used for composing simple folk or pop scores or for inspiring and challenging composers to explore new ground in their compositional practice. Fincher presents the possibility of using algorithmic systems for composing simple scores, but exposes questions of the competency of such systems in tackling more complex scores.

Akihiko Matsumoto has presented much work based in Max/MSP, which analyses composition on several levels, to generate pieces of impressive quality, often based on the styles of other composers such as Brian Eno, David Cope and John Adams [15].

3.2 Dance and Hip-Hop Music CAAC has been explored in many genres, the musical aesthetics of which differ greatly from those of Cope and Western classical. The core principles, however, remain

largely the same throughout the spectrum of popular music.

Similar methods to those that Cope uses with Emily Howell have been employed in creating mainstream music. Nick Collins has explored the idea of using programs such as SuperCollider as a compositional tool for dance music [17]. With this application, the computer will generate a beat, which the composer can then choose to use or alter. The system will then proceed to carry out other tasks such as cutting the breakbeat according to requirements set out by the user. Essentially, the system is attempting to automate some of the processes for the musician, in order to simplify the compositional process. Collins points out that, although the program is tailored to meet the requirements of dance music, the applications of such a system could extend to any musical genre. It is possible, then, to imagine such a system applied to pop music where a simple automated melody and rhythm section may create the basis for an instant track.

In his Masters thesis, Paul Maurer presents a program created in Max/MSP, which will automatically generate a piece of contemporary urban music based on a small number of input parameters [18]. The suggested practical applications of the program include overcoming writer’s block, composing for film and TV, generating audio loops for production and creating unique pieces, with which vocalists can practice. The author specifically points out that the program is not intended to be a ‘hit maker’, but to be used, instead, as a tool for generating new ideas. This suggests that algorithmic composition in popular music is guided by a moral onus to assume creative responsibility of systems that may present the possibility of automated creativity.

CAAC work by dance artists such as Mike Foyle can successfully create a “very obvious sense of progression and development”, while also manipulating timbre and other properties of synthesized sound [15]. This kind of music could arguably be deemed indeterminable from the arrangement of a human composer.

3.3 Applications to functional music As not all musical content is considered to strive for artistic merit [19], there must also be practical applications of such a system of automated composition. Telephone systems need “on-hold” music; elevators and restaurants need background music, as do video games etc. Several projects have attempted to address these needs, in what would seem to be a much less controversial bid than that of David Cope.

Nick Collins described a program designed in SuperCollider to automatically produce an entire electronic dance song [20]. Listening critically to the output from the program, and considering the conclusion of Collins’ paper, suggests that this system primarily lends itself to tasks other than composition for mainstream consideration. Alternative applications postulated for the program are

automated generation of pieces for installations, or generative, unpredictable karaoke music. Although this program does not present itself as a comprehensive compositional tool, it does outline a basis for building a system capable of generating intelligent scores for multiple instruments.

Heather Chan and DanVentura developed a system for creating altered versions of existing music to suit a certain user-specified mood [21]. The application of this system is based on theme music for TV, film and video games. This idea is similar to a classic “theme and variation” approach to composition. The advantage here is that the computer will analyse a main piece, and automatically create a variation depending on the mood that the user has selected. This would mean a practically endless supply of incidental music. It would appear that this program currently sits outside the realm of mainstream music; however, the idea is one that could easily be adopted by composers, particularly for work that has a definite overall theme or concept such as a complete album of work.

Detlev Zimmerman described a system he designed to automate the generation of musical accompaniment to a multimedia presentation [22]. This system would make musical decisions based on event cues within the presentation. This would mean that a single piece could build suspense when approaching an announcement, or become less prominent during extended periods of speech etc. Consideration is given to popular music as the source for the musical inspiration. The author suggests that such a system should not be applied to the task of creating musical masterpieces, but is more suited to more menial tasks. The application of a system such as this would perhaps seem viable for TV and film work such as those proposals suggested previously by Chan and Ventura.

Folkestad, Hargreaves and Lindstrom examined the potential of using computers to teach compositional methods to students [23]. In this way, CAAC is portrayed as a tool used not only to produce a final score but also to educate a new generation of composers and to raise the standard of future composition. One would imagine that it would seem a logical argument that a properly implemented system of CAAC could inspire composers to improve their work and explore new musical ground, rather than become lethargic and submit to the creativity of computers.

4. Creativity – The property of humans? – A debate

Perhaps the most influential issues governing the integration of CAAC methods into mainstream music are the moral questions that are suggested by delegating creative processes to a machine. In music, computer algorithms can be applied to a number of processes, including sampling and sound synthesis. The most hotly debated applications of such algorithms, however, are those that attempt to undertake the task of composing.

There are great philosophical implications associated with such use of computer algorithms, as they act as a replacement for processes that many feel are rightfully the territory of humans – imagination, soul, intuition etc. [1].

The main objection to computers assuming responsibility for the compositional process is that it is seen as replacing the dedicated human composer [24]. Millea and Wakefield disregarded this argument when they presented the development of algorithmic composition software, with the distinct intention of composing a song with the potential to become popular [25]. In order to achieve this, the system analysed existing popular songs to identify properties that may contribute to the memorable nature of the song. Their paper could be seen as important due to the specific attention that it gives to popular music, but also due to the moral questions that such a system might provoke. If the paper’s suggestion of searching for a hit were taken seriously, there would undoubtedly be issues with the intent of the artists. It would appear that such motives may be considered acceptable in a theoretical sense, but would be met with great scorn if acted upon in a capitalist manner.

Bruce Jacob argues in support of computer algorithms as a tool for composing [26]. However, he notes the importance of a direct correlation between the work of the composer and the tasks that the algorithm will perform, so that the creativity can be truly derivative of the composer’s style and ability.

Robert Peperell questions the ability of computers to independently create viable pieces of art [27]. The author seems to be of the opinion that computers can be used to design templates for creative output, but they are scarcely intelligent enough to execute a complete piece. The example given is that of a poster – the user can specify the size of paper, text to be included and general colour theme, the computer can then generate a number of different variations on the design, but they will essentially only act as a guide for the user’s creative direction. Peperell’s argument, then, is that he does not see computers as having the creative ability to replace a human composer.

Peperell’s argument is rejected by David Cope, stating that those who do not believe that creativity can be derived from computer programs have likely defined creativity to such an exclusive, narrow extent that even humans could not claim to create [13]. Assuming that such computational creativity is possible, Cope is likely to have come closest to its mastery. The reactions of the musical establishment to Cope’s work with Emmy, in creating pieces reminiscent of Bach and Vivaldi, were those of disgust and vitriol [28]. The work was lambasted for having no humanity or “depth of feeling”. The performance of a new, synthetically created Bach piece was considered blasphemy [12]. It could be argued that much of the opposition to Cope’s work stemmed from the fact that the establishment’s sense of self was challenged by the composer leaving audiences unable to distinguish between Emmy’s work and genuine

pieces by Bach [29]. It would appear that Cope’s algorithmic composition software is capable of passing the Turing test – something that he sees, not as a detriment to humanity, but a great achievement, that man has created a machine, such as Deep Blue, that is complex enough to help us to understand the workings of our own thoughts and minds [30].

Doug Hofstadter admits a certain amount of despondency at the thoughts of Emily Howell becoming the future of musical creativity: “Things that touch me at my deepest core might be produced by mechanisms millions of times simpler than the intricate biological machinery that gives rise to the human soul” [30]. The question raised by many of Cope’s critics is one of our role within the musical framework. “If creativity can be mimicked, then what makes us so special?”[31]. In response, Cope has suggested that creativity comes from little more than our experiences of what has preceded us, implying that computers are also capable of such experiences, and that they can teach us about our own constitution. “Mimicry helps us to understand our humanness”[32].

Eleanor Selfridge-Field supports Cope’s approach to human discovery: “That Cope expresses the pathways to the composition of coherent works as computer algorithms should not obscure the fact that he is ultimately working on the frontiers of the human understanding of human processes” [33].

Cope sees his software as little more than an extension of himself – a mere tool that he uses as part of the process of composing [28]. In response to the argument that the work of Emily Howell is devoid of humanity and soul, Cope states that the question is not whether or not the computer possesses a soul, but if humans possess one.

“The feelings that we get from listening to music are something we produce, it’s not there in the notes. It comes from emotional insight in each of us – the music is just the trigger” [28].

5. Conclusion Throughout this paper, we have examined the history of algorithmic composition and how it fits in with traditional concepts of music and composition. Several applications of CAAC in mainstream media have been mentioned, and their advantages and limitations suggested. The primary focus of debate remains to be the role of CAAC in the contexts of composition and artistic creation. It would appear that most staunch members of the musical establishment will continue to reject the advances in CAAC for the foreseeable future. It does, however, appear that these advances are becoming more sophisticated and convincing as compositional tools. This author would suggest that such experimentation and research be embraced, as it might aid in understanding the root of complex processes, such as the perception of beauty within music. However, it would also appear that due caution

should be expressed with the application of such advanced processes. One must consider that research and popular culture do not always coincide and that, perhaps, releasing such softwares on a commercial level could lead to a musical environment over-saturated with false genius and soulless beauty.

References [1] D. Cope, The Algorithmic Composer. Madison,

Wisconsin: A-R Editions, 2000. [2] L. Weinstein, MozartBalls, Rhombus Media

Production, 2006. [3] R. Blitstein, “Triumph of the cyber composer.”

Internet: http://www.miller-mccune.com/culture-society/triumph-of-the-cyborg-composer-8507, Feb. 22, 2010 [Feb. 11, 2011].

[4] D. Jeffcock, Howard Goodall: How Music Works, Tiger Aspect (IMG), 2006.

[5] “Computer music.” Internet: http://en.wikipedia.org/wiki/Computer_music , Feb. 12, 2011 [Feb. 15, 2011].

[6] C. Roads, The Computer Music Tutorial. Cambridge, Mass. MIT Press, 1996.

[7] “Pop music automation.” Internet: http://en.wikipedia.org/wiki/Pop_music_automation , Nov. 3 2010 [Feb 27, 2011]

[8] N. Collins, “Musical Form and Algorithmic Composition,” Contemporary Music Review, vol. 28, no. 1, pp. 103–114, 2009.

[9] F. Hedelin, “Formalising form: An alternative approach to algorithmic composition,” Organised Sound, vol. 13, no. 03, pp. 249–257, 2008.

[10] W. B. Hewlett and E. Selfridge-Field, Eds., Melodic Similarity: Concepts, Procedures, and Applications. London: The MIT Press, 1998.

[11] H. Taube, Notes from the Metalevel: Introduction to Algorithmic Music Composition. London: Taylor & Francis Group, 2004.

[12] A. Saenz, “Music Created by Learning Computer Getting Better | Singularity Hub.” Internet: http://singularityhub.com/2009/10/09/music-created-by-learning-computer-getting-better/. Oct. 9, 2009 [Apr. 10, 2011].

[13] D. Cope, Computer Models of Musical Creativity. Cambridge, Mass. MIT Press, 2005.

[14] N. Roberts, Brian Eno - Another Green World, BBC Arena, 2010.

[15] “Algorithmic Composition.” Internet: http://wn.com/algorithmic_composition , Feb. 23, 2010 [Feb. 23, 2010].

[16] J. Fincher, "Automated composition of musical scores based on the style of previous works", M.Sc. thesis, Appalachian State University, Boone, North Carolina, 2006.

[17] N. Collins, “Algorithmic Composition Methods for Breakbeat Science,” Proceedings of Music Without Walls, 2001.

[18] P. B. Maurer, "Generating a Genre: An Algorithmic Approach to Creating Popular Music," M.A. thesis, New York University, New York, 2009.

[19] C. Lowe & I. Hotchkiss, “YouTube - Rebecca Black - Friday (OFFICIAL VIDEO).” Internet: http://www.youtube.com/watch?v=CD2LRROpph0. [Apr. 11, 2011].

[20] N. Collins, “Infno: Generating Synth Pop and Electronic Dance Music On Demand,” in Proceedings of the International Computer Music Conference, 2008.

[21] H. Chan and D. Ventura, “Automatic Composition of Themed Mood Pieces,” in Proceedings of the 5th International Joint Workshop on Computational Creativity, 2008, pp. 109–115.

[22] D. Zimmerman, "Automatic Composition of Intention-Based Music for Multimedia Interface," in Proceedings of the First International Workshop on Intelligence of Multimedia Interfaces (IMMI1), 1995.

[23] G. Folkestad, D. J. Hargreaves, and B. Lindstrom, “Compositional strategies in computer-based music-making,” British Journal of Music Education, vol. 15, no. 01, pp. 83–97, 1998.

[24] A. Pascoe, "Algorithmic Composition," M.Sc. thesis, University of California, Santa Cruz, 2009.

[25] T. A. Millea and J. P. Wakefield, “Automating the Composition of Popular Music: The Search For a Hit,” in Proceedings of Computing and Engineering Annual Researchers' Conference (CEARC), 2009, pp. 45-50.

[26] B. L. Jacob, "Algorithmic Composition As a Model of Creativity," Organised Sound, vol.1, iss. 3, 1996.

[27] R. Pepperell, “Computer aided creativity: practical experience and theoretical concerns,” in Proceedings

of the 4th conference on Creativity & cognition, 2002.

[28] T. Adams, “David Cope: ‘You pushed the button and out came hundreds and thousands of sonatas’, Technology, The Observer.” Internet: http://www.guardian.co.uk/technology/2010/jul/11/david-cope-computer-composer. Jul. 11, 2010 [Apr. 10, 2011].

[29] J. Hsu, “Is the World’s Most Intelligent Music Composing Software as Creative as Bach? | Popular Science.” Internet: http://www.popsci.com/technology/article/2010-02/composers-music-making-machine-stirs-controversy-about-creative-originality. Feb. 25, 2010. [Apr. 10, 2011].

[30] D. Cope, Virtual Music: Computer Synthesis of Musical Style. Cambridge, Massachusetts: The MIT Press, 2001.

[31] S. Yee, “Computer aided composition that sounds like composers of yesterday.” Internet: http://www.keyofgrey.com/2010/02/computer-aided-composition-that-sounds-like-composers-of-yesterday, Feb. 26, 2010 [Feb. 18, 2011].

[32] C. Watson, “Composer Emily Howell may lack flesh and blood, but her creator David Cope has given her that most human of traits: creativity - Santa Cruz Sentinel.” Internet: http://www.santacruzsentinel.com/ci_14330754. Apr. 2, 2010 [Apr. 10 2011].

[33] “Computer Models of Musical Creativity - The MIT Press.” Internet: http://mitpress.mit.edu/catalog/item/?ttype=2&tid=10661. [Apr. 10, 2011].