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George Holloway Composer 盧長劍作曲家
Metastable for string quartet ⟪亞穩態⟫（2019）為弦樂四重奏而作
TimeArt Ensemble 時間藝術工作室
（張庭碩、蔡承宏、廖培雅、張智惠）
"Ghost in the Machine", George Holloway Composer Portrait Concert, National Recital Hall, Taipei, Taiwan, 9th April '24.
盧長劍作曲家肖像音樂會「機械中的幽靈」，2024年4月9日在台灣國家演奏廳
Movements:
I. Godric of Finchale (c.1065-1170)
II. Henry Purcell (1659-1695)
III. Jean-Philippe Rameau (1683-1764)
IV. Modest Mussorgsky (1839-1881)
V. Morton Feldman (1926-1987)
(Scroll down for English programme note)
創作與2019年的弦樂四重奏 ⟪亞穩態⟫（Metastable） 是盧長劍與佈裡斯托爾大學的「互動性與圖形研究小組」以及化學系的學者Lex O’Brien和Alex Jones共同舉行的研究項目「Metastable Impressions」中的創作成果。 該作品於 2019 年 5 月由英國著名的樂隊利蓋蒂弦樂四重奏在布里斯託大學維多利亞報告室的講座音樂會中首演，同時有投影由研究合作伙伴製造的動畫影片，與音樂完美地同步。
⟪亞穩態⟫既是數據聽覺化，也是自主的藝術作品。這一類音樂也曾經稱之為「受數據啟發的音樂」（data-inspired music）。 化學系的學者提供了透過機器學習模擬產生的蛋白質分子的微觀振動數據，為了使用音樂與影片來同時呈現。雖然 ⟪亞穩態⟫無意作為數據分析的工具，也只是間接地用於科普目的的，但音樂的材料依然一直很密切地追蹤所提供的數據之主要發生和演變。由於這種「受數據啟發的音樂」不可能是完全「自主的」（就是說，不能依據其自身的節奏與發展邏輯闡述材料），反而要跟隨「音樂之外」的數據結構，音樂就呈現出一種很玄妙的美學特征，即「客體化」。
⟪亞穩態⟫與它相應的動畫，還具有另一層的創作意涵。負責數據視覺化的學者Lex O’Brien挑選了來自五個不同時期與地理位置的五位畫家，並透過機器學習的「風格遷移」，使動畫的部分依次呈現這五位畫家的畫風。盧長劍從這個過程中汲取了靈感，並以同樣的方式分別挑選了來自五個不同時期與地理位置的作曲家之音樂作為⟪亞穩態⟫五個樂章的基本素材或樂思，並透過這五種不同的「風格鏡頭」，「投射」了數據（五個樂章的標題都命名為這五位作曲家的名字及生卒年份）。通過該方式，觀眾所聽到的和看到的，不僅在純粹呈現數據的內容，它也有暗示某一個時代和地方的音樂和繪畫的風格。
在 ⟪亞穩態⟫的首演時，數據的聽覺化（音樂）和視覺化（動畫）同步呈現了。但由於作曲家很努力譜寫不僅僅在呈現數據，而本身富有趣味和藝術性的音樂，盧長劍然而決定在本場音樂會中只呈現 ⟪亞穩態⟫的音樂部分。
This project that gave birth to this piece, "Metastable Impressions", was conceived with collaborators in the Interaction and Graphics Research Group and in the Chemistry Department of Bristol University, funded by the Jean Golding institute. Metastable was premiered by the Ligeti String Quartet in a lecture-recital in Victoria’s Room (sic), The Victoria Rooms, Bristol University, in May 2019.
My main role in the project was to compose a piece of music that was both data-sonification and an autonomous artwork- a sort of data-inspired music. The resulting work was Metastable.
The piece, while not in any way intended as a data-scanning tool, and only weakly intended as a public science-communication tool, nonetheless is ineradicably bound to the underlying scientific data. I had to be careful that there were processes of tension and change in the music evocative of the physical processes at work in the molecular dynamics, and this stricture was made even more acute by the combination of the music with a projected visualisation of the molecule. The music could not therefore be completely autonomous (freely treating the material in its own time and following its own development), but had necessarily to conform to the same time structure and transformations of material to which the visualisation conformed, as dictated by the underlying data.
In choosing the portions of data to be sonified (the “trajectories”), it was clear that the data would entirely preclude a “traditional” musical syntax and phraseology, but this ensured that the five movements that made up Metastable naturally took on unexpected and spontaneous-feeling structures.
One final aspect to mention is the use of style-transfer in both the visualisation (using machine-learning) and in the musical composition (done “manually” by me as the composer). My stylistic use of earlier composers, such as Purcell and Mussorgsky, sits in a time-honoured tradition of musical borrowing.
Read more about Metastable Impressions at the blogspot on the Jean Golding Institute website:
jeangoldinginstitute.blogs.bristol.ac.uk/2019/07/15/metastable-impressions/?_ga=2.269229362.299420602.1569810288-1205396178.1559033262