The Rarity of Musical Audio Signals Within the Space of Possible Audio Generation

• URL: http://arxiv.org/abs/2405.15103v1
• Date: Thu, 23 May 2024 23:25:46 GMT
• Title: The Rarity of Musical Audio Signals Within the Space of Possible Audio Generation
• Authors: Nick Collins,
• Abstract summary: A white noise signal can access any possible configuration of values, though statistically over many samples tends to a uniform spectral distribution. The probability that white noise generates a music-like signal over different durations is analyzed. The applicability of this study is not just to show that music has a precious rarity value, but that examination of the size of music relative to the overall size of audio signal space provides information to inform new generations of algorithmic music system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A white noise signal can access any possible configuration of values, though statistically over many samples tends to a uniform spectral distribution, and is highly unlikely to produce intelligible sound. But how unlikely? The probability that white noise generates a music-like signal over different durations is analyzed, based on some necessary features observed in real music audio signals such as mostly proximate movement and zero crossing rate. Given the mathematical results, the rarity of music as a signal is considered overall. The applicability of this study is not just to show that music has a precious rarity value, but that examination of the size of music relative to the overall size of audio signal space provides information to inform new generations of algorithmic music system (which are now often founded on audio signal generation directly, and may relate to white noise via such machine learning processes as diffusion). Estimated upper bounds on the rarity of music to the size of various physical and musical spaces are compared, to better understand the magnitude of the results (pun intended). Underlying the research are the questions `how much music is still out there?' and `how much music could a machine learning process actually reach?'.

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