Testing cognitive theories by creating a pattern-based probabilistic algorithm for melody and rhythm in jazz improvisation.

Abstract

Building on previous work, which suggests that jazz improvisers insert patterns stored in procedural memory, a probabiMstIc model based on patterns from a corpus of Charlie Parker solos was developed and implemented. In previous analysis, patterns were detected in the corpus in significant proportions; however, the results of a parallel control situation showed minimal patterns. The control improvisation was generated by software based on grammars and contours, coincident with the cognitive position that emphasizes learned rule-based procedures in improvisation, as opposed to stored patterns. The present pattern-based improvisations, using our model, have graphs that coincide significantly with the actual human improvisation. Though briefly described earlier (Norgaard, Montiel, & Spencer, 20t3), the current article expands the theoretical foundation and adds methods for evaluating our algorithm using interval distributions and alternate corpora. Specifically, we show that the algorithm is capable of generating improvisations in fiddle and classical styles, demonstrating that the pattern-based algorithm is style independent. Our model shows much promise both for future research in the cognitive underpinnings of musical improvisation as well as for the development of software based on a stylistically appropriate concatenation of actual patterns.Perfortnance of preexisting tnusic and mtjsical improvisation both involve learned movements. However, during mu.sical improvisation, the exact configuration of those movements is determined in the moment. How is this accomplished? What information is stored in the improviser's brain that enables this complex behavior? One theory posits memorized schémas form the basis for the improvised output (Pressing, 1988), while a competing theory emphasizes learned rules (Johnson-Laird, 2002). The current project further explores these questions through the implementation of a computer algorithm for improvisation based on the principle advocated by Pressing. We compare output from our model with the results of a jazz analysis study as well as with the output of a competing model that uses a rule-based algorithm to generate melodies in a jazz style. In addition, we show that our algorithm is capable of generating melodic output in other styles given a corpus in that style.Pressing's (1988) model of the cognitive processes underlying improvisation is still widely cited (

The neuroscience of musical improvisation

“…Several innovative methods have already been developed to study improvisation behaviorally (e.g., Goldman, 2013;Norgaard, 2011Norgaard, , 2014Norgaard et al, 2013). Improvisation researchers would also benefit from embracing new and emerging methods in cognitive neuroscience.…”

Pedagogical applications of cognitive research on musical improvisation

“…Through the practice of improvisation, a database of schemes and motor programs is developed that can be used during a performance. Preformed structures stored in procedural memory are inserted during improvisation ( Norgaard et al, 2013 ). Improvisation is acted on in real-time, and all of the actions require information processing, which can be coordinated and accelerated to obtain a fluent performance.…”

"If You Have to Ask, You'll Never Know": Effects of Specialised Stylistic Expertise on Predictive Processing of Music

“…Given that improvisation relies on real-time composition comprising uninterrupted, serial music making under strict time constraints [ 35 ], it provides a highly reliable manifestation of the bebop style as it is cognitively represented. Whereas previous studies have emphasised productive aspects with the explicit aim of modelling the cognitive processes underlying improvisation [ 35 , 40 , 41 , 50 – 52 ], less is known about expert perception of such stimuli. Most probably, listening to improvised bebop solos requires highly specialised stylistic expertise while circumventing dichotomous expectations arising from veridical knowledge.…”

“…Four specific aspects of IDyOM enable us to acknowledge and capitalise upon previous findings while maintaining cognitive plausibility. First, successful replications of the bebop style from its inherent probabilistic properties [ 50 – 52 , 54 ] call for the use of unsupervised statistical learning as implemented in IDyOM. Next, whereas previous studies have modelled bebop improvisation with zeroth-order [ 54 ] or higher-order, conditional statistics [ 50 , 52 ], IDyOM accomplishes even greater predictive power by optimally combining Markov models of variable context lengths [ 53 ].…”