Imaginary Miles: Modeling Musebots after Musicians Arne Eigenfeldt School for the Contemporary Arts Simon Fraser University, Vancouver, Canada [email protected] ABSTRACT ing and simulation [4]. One facet of computational musi- cology is to prove the validity of the derived model Musebots are autonomous musical agents that interact through generation [5]; however, it should be pointed out with other musebots to create music. Inaugurated in that its specific goals are different from those of the pro- 2015, musebots are now an established practice in the duction systems found within MuMe, in that the resulting field of musical metacreation, which aims to automate generation is not meant to have artistic validity. aspects of creative practice. Musebots have been present- The work described in this paper is firmly within the ed as continuously running installations, in which ensem- MuMe field: while using methods derived from machine- bles, curated from the pool of community-coded agents, learning to produce a loose model of a specific stylistic interact autonomously with one another for five to seven corpus, the goal is not to exactly reproduce the model; minutes, emphasising their (often) disparate styles. Imag- instead, the model is a starting point for more general inary Miles, while continuing the musebot tradition of compositional explorations of musical agent relation- open-source development, presents musebots coded only ships. by the author. Furthermore, the generated style is quite explicit – late 1960s jazz, specifically the Miles Davis 1.3 Musebots groups of that era. While individual musebots are mod- elled after specific musicians, Imaginary Miles is not an The open source musebot protocol1 was originally devel- effort at computational musicology that attempts to per- oped to coordinate generative music software ensembles fectly replicate its models; instead, the ensemble’s unique [6] and facilitate modularised prototyping of designs [7]. interaction and group improvisation is the basis of the The protocol is, at its heart, a method of communicating exploration. states and intentions, sending networked messages estab- lished through a collaborative document via OSC [8]. 1. INTRODUCTION Each musebot has tended to correspond to a single part, or layer, within the resulting music: for example, a gener- ator of bass lines, or adaptive drumbeats. The protocol 1.1 Musical Metacreation suggested that “if we make these agents smart, then the Musical metacreation, or MuMe, is a subfield of compu- resulting music will be coherent and continually evolving tational creativity, and is the art of “endowing machines in interesting ways”, which has been argued as being the with the ability to achieve creative musical tasks” [1]. It case [9, 10]. explores creativity as it is, and as it could be, and is popu- The self-organised model of the musebot ensemble that lated by a diverse group of researchers, including psy- emerged limited the duration of any given ensemble’s chologists, cognitive scientists, artificial intelligence re- performance in the ongoing installations to between five searchers, machine learning specialists, and, perhaps and seven minutes. This limitation was entirely musical, most importantly, artists. There have been a number of in that the surface interaction between musebots – mainly impressive MuMe production systems that produce musi- beat, harmony, and density – provided limited longer cal output, either off-line in the form of symbolic repre- scale interest [11]. Subsequent code-jams between muse- sentation – e.g. Cope’s EMI [2] – or on-line – e.g. Lewis’ bot developers investigated the potential for musebots to Voyager [3]. These systems have used a variety of meth- determine endings to improvisations [12] as well as turn- ods, such as Lewis’ use of improvisational heuristics, or taking [13]. This latter research, in which multiple muse- Cope’s use of machine-learning to derive production bots created by five coders listened to one another and rules from a corpus of style-specific music. More recent- negotiated when to solo, led to the research described ly, Google’s Magenta Project (magenta.tensorflow.org) here; in this paper, the entire musebot ensemble is created has explored Deep Learning in music creation. by the author in an effort to explore a homogeneous, ra- ther than heterogeneous, musical style. 1.2 Computational Musicology Using computational methods to examine a corpus bor- 2. MILES DAVIS GROUPS 1968-70 rows from the related field of computational musicology, Miles Davis is considered to be one of the preeminent which is the study of music by means of computer model- figures in American jazz, spearheading several innovative Copyright: © 2018 Arne Eigenfeldt. This is an open-access article dis- 1 tributed under the terms of the Creative Commons Attribution License 3.0 tinyurl.com/ph3p6ax Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. movements between 1949 and his death in 1992 [14, 15]. and rhythm, Davis “kept one foot planted in inherited His second great quintet from 1963-1968 – Miles on forms and the other in the new order” [19]. Even in the trumpet; Wayne Shorter, saxophone; Herbie Hancock, much freer tunes of 1970’s Bitches Brew, a strong pulse piano; Ron Carter, bass; Tony Williams, drums – defined is retained, and at least some semblance of a constraining the post-bop style that explored more formally and har- harmony, albeit static. monically free playing [16], leading eventually to the As the distinctive head melodies and cyclical chord fusion of jazz and rock with the release of Bitches’ Brew progressions disappeared, so did the focus on a single in 1970 [17]. He and his band recorded several seminal soloist during the improvisatory sections. While the sup- albums during this period (see Table 1), documenting the porting roles of the rhythmic section – piano, bass, and transition between the two styles2. drums – continued to allow for individualistic commen- tary on a soloist’s material, the freedom of the rhythm Album Recording date section to dictate direction greatly increased. This be- Miles in the Sky January/May 1968 came particularly evident in the interaction between pi- Filles de Kilimanjaro June 1968 ano and drums in the Lost Quintet: “the potential to fore- ground rhythmic interplay between (pianist) Corea and In a Silent Way February 1969 (drummer) DeJohnette increased, and space had to be Bitches Brew August 1969 consciously made for (bassist) Holland to be heard and for Corea and Holland to interact” [19]. The focus of the Table 1. Seminal albums during Miles Davis’ transition music became less about the soloist, and more about the from post-bop to fusion. interaction between the musicians: “audiences expecting Along with the aforementioned loosening of accepted a one-to-one correspondence between soloist and accom- constraints regarding harmony and form during this time, paniment would cease to follow the logic” [19]. Davis initiated electric instruments3 into his group, as Corea describes Davis as a “chemist” when it came to well the use of straight-eighth (“rock”) rhythms, both first selecting the musicians of his ensemble, particularly dur- heard on “Stuff”, recorded May 1968 on Miles in the Sky. ing this time: rather than compose melodies and harmo- nies for any musician to play, “he chose these guys so 2.1 Expanded Composition that it went together in a way that he heard it” [19]; in other words, he curated an ensemble of possibilities and Although the recordings from 1968-69 can be viewed as probabilities. Such curation is exactly how musebots can transitional between his mid-60s post-bop and 1970s be honed for interaction. jazz-rock fusion, they offer a new, and seemingly unful- filled, exploration of extended composition. As Waters 3. DESCRIPTION points out, compositions like “Stuff” and “Tout de Suite” are 40-bar and 70-bar forms, respectively, with unusual Imaginary Miles explores models of group improvisation repetitions and derivations within the melodic restate- found within the 1968-1970 Miles Davis groups, as well ments: “the alterations made to successive melodic as the extended compositional forms found on the 1968 statement create a sense of shifting perspective upon re- recordings. Several of the musebots are modeled after curring melodic objects” [18]. However, Davis pursued a specific musicians, although not necessarily from that different direction by the time of the recording of Bitches particular period. For example, a Mini-Moog musebot Brew, focusing upon sculpting interactions within the emulates Chick Corea’s playing in his Return to Forever group, rather than dictating longer melodic ideas [19]. period (specifically 1974-76), and an electric guitar musebot emulates John McLaughlin’s Mahavishnu Or- 2.2 Group Improvisation chestra (1971-74) playing style; as such, the ensemble is very much imaginary. Although many of the modeling In 1959, Ornette Coleman released The Shape of Jazz to attributes are heuristic and decisions made through re- Come, one of the first albums that explored collective and peated personal listening and aural analysis, aspects of democratic improvisation [19]; this record deeply affect- machine learning are also used. ed many of the late bebop musicians, including Davis. By the mid-1960s, Davis’ exploration of more structural 3.1 Corpus-based melody and harmony freedom was a reaction to the “overabundance of chords” found in late bebop, requiring new notions of how to Imaginary Miles retains the earlier structural model of “balance freedom of the individual with membership in a head-solos-head found on Miles in the Sky and Filles de collective” [19]. However, unlike Coleman who aban- Kilimanjaro. A musebot generates an overall form of doned accepted notions of structure, melody, harmony, intro – head – solos – head – outro. The length of the head, which determines the consistent phrase length used 2 These recordings are now augmented by an increasing number in a specific generated composition, is a probabilistic of bootleg recordings and official releases by Columbia in the value derived from the corpus (see Table 2).