Clarinet Multiphonics Synopsis of \"Computer

Clarinet Multiphonics

Synopsis of "Computer Analysis of Clarinet Multiphonics"

In his article "Computer Analysis of Clarinet Multiphonics," Kenneth J. Peacock provides a detailed explanation of clarinet multiphonics, their importance in current musical trends, and the use of computer imaging techniques in understanding the complexity of live clarinet tones, both standard and multiphonic.

Peacock begins his article by discussing the intentional use of multiphonics in modern clarinet composition and performance. Though multiphonics were once to be avoided in performance, studies and experiments in the second half of the 20th century by Bruno Bartolozzi and Philip Rehfeldt, among others, refined and standardized the technique of producing and controlling multiphonics. The increasing prevalence of electronic music in recent years has acclimated modern audiences to more diverse sounds and has made them more accepting of the intentional use of multiphonics in compositions.

After pointing out the inability of electronic music to replicate the complexities of a live multiphonic tone, Peacock turns to the use of digital computer imaging techniques to illustrate and reveal those complexities. These images are created using the mathematical process known as the Fast Fournier Transform. This process creates a spectrograph similar to that created when a prism divides white light into its component colors. In the case of musical spectral analysis, the components are the frequencies of the pitches present in a tone. When viewed through this spectrographic process, the clarinet is found to have a hallmark pattern: strong odd harmonics with the highest amplitudes found in the lowest frequencies.

Before continuing the analysis of the multiphonic spectrograph, Peacock acknowledges a weakness in the Fast Fournier Tranform process in imaging musical tones. The image of a tone measured over time is created by averaging the harmonic content during that time; the longer the time, the more accurate the average. However, because of its nature as an average, the image does not provide an indication of the changes in the sound over time. To fix this weakness, several images recorded milliseconds apart are superimposed on one another, creating a typographic model of the behavior of the sound over a staggered time period.