Max as a Digital Platform for Noise Music Performance

Muhamad Hafifi Mokhtar, Clare Suet Ching Chan
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Abstract

This article explores Max as a digital platform for performing noise music through a practice-led research method. The practice-led research method was used to explore the possibilities of building Max patches, while content analysis method was used to analyse the outcome of the patches. Several Max patches were created to explore the potential of Max as an alternative approach for performing noise music. Findings show that Max can replicate the audio processing methods used in conventional performance. Due to Max capabilities, some of these methods could be automated and arranged prior to the performance. In addition, Max patches featured changing sound, random pitches, mixture of pre-recorded audio source and live instrument, and drone sound combined with automatic constant real-time audio self-processing and automatic audio panning, a feature that seldom appears in the local noise music scene. In conclusion, this research argues that Max has much potential for creating a variety of digital sounds that are harsh and dissonant to the ears, therefore contributing to the musical diversity in noise music performance. These sounds are the results of the features of audio self-processing, random pitches, automatic audio panning object and self-changing pitched drone audio signals relying on random MIDI values that appeared in Max.
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Max 作为噪音音乐表演的数字平台
本文通过以实践为主导的研究方法,探讨 Max 作为表演噪音音乐的数字平台。实践主导研究法用于探索制作 Max 补丁的可能性,而内容分析法则用于分析这些补丁的结果。我们制作了几个 Max 补丁,以探索 Max 作为表演噪音音乐的另一种方法的潜力。研究结果表明,Max 可以复制传统表演中使用的音频处理方法。由于 Max 的功能,其中一些方法可以在表演前自动安排。此外,Max 片段还具有声音变化、随机音高、预录音源与现场乐器混合、无人机声音与自动恒定实时音频自我处理和自动音频平移相结合等特点,这在本地噪音音乐场景中很少出现。总之,本研究认为,Max 在创造各种刺耳、不和谐的数字声音方面具有很大潜力,因此有助于噪声音乐表演中的音乐多样性。这些声音是 Max 中出现的音频自处理、随机音高、自动音频平移对象和依靠随机 MIDI 值的自变音高无人机音频信号等特性的结果。
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来源期刊
Harmonia: Journal of Arts Research and Education
Harmonia: Journal of Arts Research and Education Arts and Humanities-Visual Arts and Performing Arts
CiteScore
0.90
自引率
0.00%
发文量
32
审稿时长
4 weeks
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