This preliminary to textural analysis poses two questions: (1) What constitutes textural stability? (2) How do textures succeed one another to produce moments of textural instability? To answer these questions, I begin with two premises. First, texture is comprised of, but ontologically distinct from, musical parameters. Second, parameters are often in states of change. Consequently, texture relies on cognitive processes that evaluate, prioritize, and normalize parametric change.

A stable texture is an emergent structure in which some parameters take perceptual priority over others and normative ranges or rates of parametric change are maintained at a local level. Listeners’ decisions about hierarchy and normative change are informed by familiarity with idioms (e.g. Alberti bass), topics (e.g. learned style), and other conventions (e.g. groove). Textural instability results when parametric changes exceed listeners’ locally assigned normative values, creating transitional areas between stable textures. These transitional areas can be abrupt (“juxtadictive”) or gradual (“predictive”), the latter employing a range of compositional processes and procedures that serve to supplant one set of norms and gradually usher in the next.

These principles are illustrated through excerpts from Mozart, Tchaikovsky, Stravinsky, and Rihanna. I conclude with some brief observations about how musical styles calibrate listeners’ expectations about parametric change.

In his keyboard Partitas, Bach blends contrapuntal (learned) with homophonic (galant) textures to an even greater extent than in many of his earlier English and French Suite dances. And rather than consistent blends of learned and galant textures, some of the Partitas exhibit a progressive evolution of textures in the dances, amounting to what I will demonstrate as textural developing variation in support of an emergent expressive trajectory, with consequences for performance.

The second strain of the Courante from the fourth Partita launches a three-stage motivic/textural development, leading from (1) stricter imitative texture (of an inverted variant of the subject), moving via its fanfare fragments to (2) a lyrical, treble-dominated texture, where descending soprano gestures are answered by the fanfare fragment of the subject, and culminating in (3) a climactic passage involving an emergent melodic line embroidered by imitative play of the fanfare fragment and its inversion. This evolution of textures effectively leads from stricter learned style, to freer galant, to an integration of aspects of each.

Bach’s textural ingenuity in turn supports (and is motivated by) a wide-ranging emotional journey, progressing first from (1) an authoritatively euphoric opening to (2) a more inward, sighing melodic line in sequential descent, embellished by lighter responses of fanfare fragments from the subject. After a sober cadence in E minor, (3) a registrally and rhythmically replete texture provides an exuberant sense of fulfillment, as an emergent melody appears to be laminated over the now-thrilling embellishments of arpeggiations from the fanfare.

This unfolding developing variation of texture, with its goal of plenitude, supports a more congruently expressive performance (as opposed to the outmoded implications of a “single affect”). I will historically ground my translation to the modern piano by avoiding use of the damper pedal, by maintaining a consistent tempo, and by applying the double-dotted verve established already by the opening movement’s French-inspired Ouverture. I will nevertheless explore the piano’s greater range of articulation and dynamic subtlety (bearing in mind Bach’s keen interest in Silbermann’s forte-pianos) in order to help project the intricate textural and emotional trajectory of this remarkable excerpt.

In orchestras, choirs, bands, and other groups, musical textures are co-created in real time by performers. Texture in ensemble music, then, reflects patterns of interpersonal coordination. I investigate these patterns using social network analysis, bringing established methods from sociology and related disciplines into music theory. I construct networks where nodes correspond to parts in an ensemble. Pairs of nodes are connected by weighted links that represent the proportion of shared onsets, an important cue for auditory streaming (Huron 1989; Bregman 1990; Duane 2013; De Souza 2019). Social network analysis can be used to investigate the organization of an entire network (i.e., the overall texture), smaller clusters within it (i.e., distinct textural streams), and the connectedness of individual nodes—as well as changes in these parameters over time. As such, social network analysis supports a dynamic, relational approach to ensemble textures.

I illustrate this approach by analyzing Hank Levy’s big band composition Whiplash (1973, arranged 2014). Whiplash is known for its rhythmic complexity, yet textural development is also essential to the piece. Its opening juxtaposes fanfare-like horns and a rock-inflected rhythm section. This antiphony creates a network with disconnected clusters. Next, the texture is greatly reduced, and network density drops: after the bass establishes a driving ostinato, the main theme is presented in a small combo texture, with alto saxophone and trumpet in unison, accompanied by the rhythm section. A process of textural accumulation emerges in subsequent passages, adding a saxophone soli, later joined by trumpets and trombones. Network density peaks in the first tutti passage, as the piece shifts from Dorian to Mixolydian mode and sets up a climactic, polyphonic return of the main theme. Here social network analysis helps show how Whiplash gradually assembles the large jazz ensemble.