AUTOMATED MUSIC COMPOSING WITH NORMAL RANDOM NUMBERS DISTRIBUTION AS “INFORMATIONAL DNA” SEQUENCING
DOI:
https://doi.org/10.18372/2310-5461.42.13749Keywords:
music composition, music composing software, neural networkAbstract
There are approximately 3 billion of the base pairs in a human genome, a complete set of deoxyribonucleic acid (DNA), made of two twisting paired strands, consists of four nucleotide bases - adenine (A), thymine (T), guanine (G) and cytosine (C). At the same time chromatic musical scale consists of twelve pitches, forming a 12-tone equal temperament, which is today a most often used temperament in Western music. It is clear that one can interpret these 12 tones sequence as a sort of “informational DNA nucleotides” sequence. It’s an approach, that can change music perception for a listener and can help to compose a more cognitive, conceptual music for a composer. Biotechnical automated control system (BACS) for music composing helps the composer to produce music with less effort, and because of it to pay more attention to the nuances, and at the same time to general quality of composed music. The generalized structural scheme of BACS consists of three main building blocks: A –neural network based software “Aquarius”; C – a human composer himself; L – a listener, or a patient of music therapy impact. All three components are inseparable, C and A elements create mutual dependence, because they interact all the time in the process of music creation. They also impact the L element, because this element is undoubtedly the BACS object of impact. The random musical signal is being created with the help of standard rand() and srand() C++ functions. In the BACS “Aquarius” to this day only uniformly distributed random numbers were used. But in order to improve performance, to widen the opportunities for choosing the needed tools for a musical composition, it is possible, of course, to introduce different distributions, such as normal, Poisson, exponential, Weibull, lognormal, discrete, piecewise constant, piecewise linear etc. The c++ code for normal distribution allows to generate random numbers, managed by two main parameters – mean and standard deviation. This code has been inserted into the main code and new stochastic music has been obtained with its assistance.
References
Глушков В. М. Введение в кибернетику: монография. Киев: Изд-во Академии наук УССР, 1964. 324 с.
Вишнівський О. В. Елементи теорії обра-зів. Авіа-2002 : матеріали IV міжнародної наук.-техн. конф. (23-25 квітня 2002 р., Київ). Київ, 2002. Т.1. С.13.119- С.13.122.
Васильев В. В., Симак Л. А. Дробное ис-числение и аппроксимационные методы в модели-ровании динамических систем: научное издание. Киев: НАН Украины, 2008. 256 с.
Vishnevsky A. V. Automated music composing as informational DNA sequencing. A.V.Vishnevsky, National Aviation University, Ukraine. Proceedings of the Fourteenth International Scientific Conference AVIA-2019, Kyiv, April 23-25, 2019. P. 1-3.
Vishnevsky A.V. Advanced Approach to Usage of an Electronic Composer: Proceedings of the 8-th world congress [«Aviation in the XXI-st century». «Safety in aviation and space technologies»] , (Kyiv (Ukraine), October 10-12, 2018) / М-во осві-ти і науки України, Національний авіаційний уні-верситет. – К. : НАУ, 2018. Т.3. С. 4.1.6 – 4.1.8
Вишнівський О.В. Синтезатор звукового ряду на основі випадкового процесу. Електроніка та системи управління. 2011. №4 (30). С. 31-36.
Vishnevsky A.V. The neural scheme of an electronic composer. Електроніка та системи управління. 2013. №1 (35). С. 107-110.
Vishnevsky A.V. Self-basis operator and or-thogonal stocastic basis application for information processing. Електроніка та системи управління. 2011. №2 (28). С. 16-20.
Вишнівський О. В. Застосування компле-ксного підходу у використанні електронного композитора. Наукоємні технології. 2019. Т. 41. №1. С. 23-29.
Pseudo-random number generation. URL: https://en.cppreference.com/w/cpp/numeric/random (access date 23.03.2019).