Explore random music generator based on Short-Time Fourier Transform

Cailin Peng

doi:10.54254/2753-8818/52/2024CH0116

Research Article

Open access

Published on 20 September 2024

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Peng,C. (2024). Explore random music generator based on Short-Time Fourier Transform. Theoretical and Natural Science,52,75-80.

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Explore random music generator based on Short-Time Fourier Transform

Cailin Peng *^,1,

¹ Peabody Institute, Johns Hopkins University, Baltimore, Maryland, United States

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/52/2024CH0116

Abstract

This paper delves into the mechanism of how the Short-Time Fourier Transform (STFT) is used for generating random music. A brief review of the history of stochastic music development is at the outset. The paper contains the principle of audio digitization. This starts with how to convert a continuous audio signal into discrete samples. The Nyquist Theorem plays an important role in that process to preserve signal integrity. The Heisenberg uncertainty principle takes effect as the STFT is applied to covert these samples. It states that when converting the audio signal between the time domain and the frequency domain, the audio signal can only have the properties of one or the other. This paper categorized the audio signals into three categories based on their spectral characteristics. The paper also points out the reason why natural sound effects are always chosen to generate random music due to their inherent complexity and randomness. This paper demonstrates the detail of STFT in generating random music, explaining how to specifically manipulate spectrum analysis to generate random music with practical examples. The paper concludes by discussing the possible future role and direction of STFT in the field of stochastic music.

Keywords

Short-time Fourier transform, Stochastic music, Random music, Audio analysis

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References

[1]. Torrésani B. (1999). Time-Frequency and Time-Scale Analysis. Academic Press, 55.

[2]. Jessop S. (2017). The Historical Connection of Fourier Analysis to Music. The Mathematics Enthusiast, 14(1), 85.

[3]. Harvey A. F, Cerna M. (1993). The fundamentals of FFT-Based Signal Analysis and Measurement in LabVIEW and LabWindows. National Instruments, 34055B-01, 7-8.

[4]. Hammond J. (2011). Mathematics of Music. UW-L Journal of Undergraduate Research XIV, 4.

[5]. Lenssen N & Needell D. (2014). An Introduction to Fourier Analysis with Applications to Music. Journal of Humanistic Mathematics, 4, 72-91.

[6]. Smith J O. (2007). Mathematics of the Discrete Fourier Transform (DFT), W3K Publishing.

[7]. Boashash B. (2003). Time-Frequency Signal Analysis and Processing: A Comprehensive Reference. Protoavis Productions. 4.

[8]. Elbir A, İlhan H. O, Serbes G, & Aydın N. (2018). Short Time Fourier Transform Based Music Genre Classification. Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), 1-4.

[9]. Lostanlen V, Andén J, Lagrange M. (2019). Fourier at the heart of computer music: From harmonic sounds to texture. Comptes Rendus Physique, 20(5), 463.

[10]. Toshniwal T, Tandon P, Nithyakani P. (2022). Music Genre Recognition Using Short Time Fourier Transform And CNN. 2022 International Conference on Computer Communication and Informatics (ICCCI), 1-4.

Cite this article

Peng,C. (2024). Explore random music generator based on Short-Time Fourier Transform. Theoretical and Natural Science,52,75-80.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-MPCS 2024 Workshop: Quantum Machine Learning: Bridging Quantum Physics and Computational Simulations

Conference website: https://2024.confmpcs.org/

ISBN：978-1-83558-621-1(Print) / 978-1-83558-622-8(Online)

Conference date: 9 August 2024

Editor：Anil Fernando, Marwan Omar

Series: Theoretical and Natural Science

Volume number: Vol.52

ISSN：2753-8818(Print) / 2753-8826(Online)

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