Artificial Intelligence - Algorithmic Composition And Generative Music.

 

A composer's approach for producing new musical material by following a preset limited set of rules or procedures is known as algorithmic composition.

In place of normal musical notation, the algorithm might instead be a set of instructions defined by the composer for the performer to follow throughout a performance. 

According to one school of thinking, algorithmic composition should include as little human intervention as possible.

In music, AI systems based on generative grammar, knowledge-based systems, genetic algorithms, and, more recently, deep learning-trained artificial neural networks have all been used.

The employment of algorithms to assist in the development of music is far from novel.

Several thousand-year-old music theory treatises provide early examples.

These treatises compiled lists of common-practice rules and conventions that composers followed in order to write music correctly.

Johann Joseph Fux's Gradus ad Parnassum (1725), which describes the precise rules defining species counter point, is an early example of algorithmic composition.

Species counterpoint presented five techniques of composing complimentary musical harmony lines against the primary or fixed melody, which was meant as an instructional tool.

Fux's technique gives limited flexibility from the specified rules if followed to the letter.

Chance was often used in early instances of algorithmic composition with little human intervention.

Chance music, often known as aleatoric music, dates back to the Renaissance.

Mozart is credited with the most renowned early example of the technique.

The usage of "Musikalisches Würfelspiel" (musical dice game) is included in a published manuscript claimed to Mozart dated 1787.

In order to put together a 16-bar waltz, the performer must roll the dice to choose one-bar parts of precomposed music (out of a possible 176) at random.

John Cage, an American composer, took these early aleatoric approaches to a new level by composing a work in which the bulk of the composition was determined by chance.

In the musical dice game, chance is only allowed to affect the sequence of brief pre-composed musical snippets, but in his 1951 work Music of Changes, chance is allowed to govern almost all choices.

To decide all musical judgments, Cage consulted the ancient Chinese divi nation scripture I Ching (The Book of Changes).

For playability considerations, his friend David Tudor, the work's performer, had to convert his highly explicit and intricate score into something closer to conventional notation.

This demo shows two types of aleatoric music: one in which the composer uses random processes to generate a set score, and the other in which the sequence of the musical pieces is left to the performer or chance.

Arnold Schoenberg created a twelve-tone algorithmic composition process that is closely related to fields of mathematics like combinatorics and group theory.

Twelve-tone composition is an early form of serialism in which each of the twelve tones of traditional western music is given equal weight.

After placing each tone in a chosen row with no repeated pitches, the row is rotated by one at a time until a 12 12 matrix is formed.

The matrix contains all variants of the original tone row that the composer may use for pitch material.

A fresh row may be employed once the aggregate—that is, all of the pitches from one row—has been included into the score.

Instead of writing melodic lines, the rows may be further separated into subsets to provide harmonic content (a vertical collection of sounds) (horizontal setting).

Later composers like Pierre Boulez and Karlheinz Stockhausen experimented with serializing additional musical aspects by building matrices that included dynamics and timbre.

Some algorithmic composing approaches were created in response to serialist composers' rejection or modification of previous techniques.

Serialist composers, according to Iannis Xena kis, were excessively concentrated on harmony as a succession of interconnecting linear objects (the establishment of linear tone-rows), and the procedures grew too difficult for the listener to understand.

He presented new ways to adapt nonmusical algorithms for music creation that might work with dense sound masses.

The strategy, according to Xenakis, liberated music from its linear concerns.

He was motivated by scientific studies of natural and social events such as moving particles in a cloud or thousands of people assembled at a political rally, and he focused his compositions on the application of probability theory and stochastic processes.

Xenakis, for example, used Markov chains to manipulate musical elements like pitch, timbre, and dynamics to gradually build thick-textured sound masses over time.

The likelihood of the next happening event is largely influenced by previous occurrences in a Markov chain; hence, his use of algorithms mixed indeterminate aspects like those in Cage's chance music with deterministic elements like serialism.

This song was dubbed stochastic music by him.

It prompted a new generation of composers to incorporate more complicated algorithms into their work.

Calculations for these composers ultimately necessitated the use of computers.

Xenakis was a forerunner in the use of computers in music, using them to assist in the calculation of the outcomes of his stochastic and probabilistic procedures.

With his album Ambient 1: Music for Airports, Brian Eno popularized ambient music by building on composer Erik Satie's notion of background music involving live performers (known as furniture music) (1978).

The lengths of seven tape recorders, each of which held a distinct pitch, were all different.

With each loop, the pitches were in a new sequence, creating a melody that was always shifting.

The composition always develops in the same manner each time it is performed since the inputs are the same.

Eno invented the phrase "generative music" in 1995 to describe systems that generate constantly changing music by adjusting parameters over time.

Ambient and generative music are both forerunners of autonomous computer-based algorithmic creation, most of which now uses artificial intelligence techniques.

Noam Chomsky and his collaborators invented generative grammar, which is a set of principles for describing natural languages.

The rules define a range of potential serial orderings of items by rewriting hierarchically structured elements.

Generative grammars, which have been adapted for algorithmic composition, may be used to generate musical sections.

Experiments in Musical Intelligence (1996) by David Cope is possibly the best-known use of generative grammar.

Cope taught his program to produce music in the styles of a variety of composers, including Bach, Mozart, and Chopin.

Information about the genre of music the composer desires to replicate is encoded as a database of facts that may be used to develop an artificial expert to aid the composer in knowledge-based systems.

Genetic algorithms are a kind of composition that mimics the process of biological evolution.

The similarity of a population of randomly made compositions to the intended musical output is examined.

Then, based on natural causes, artificial methods are applied to improve the likelihood of musically attractive qualities increasing in following generations.

The composer interacts with the system, stimulating new ideas in both the computer and the spectator.

Deep learning systems like generative adversarial networks, or GANs, are used in more contemporary AI-generated composition methodologies.

In music, generative adversarial networks pit a generator—which makes new music based on compositional style knowledge—against a discriminator, which tries to tell the difference between the generator's output and that of a human composer.

When the generator fails, the discriminator gets more information until it can no longer distinguish between genuine and created musical content.

Music is rapidly being driven in new and fascinating ways by the repurposing of non-musical algorithms for musical purposes.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Computational Creativity.

Further Reading:

 

Cope, David. 1996. Experiments in Musical Intelligence. Madison, WI: A-R Editions.

Eigenfeldt, Arne. 2011. “Towards a Generative Electronica: A Progress Report.” eContact! 14, no. 4: n.p. https://econtact.ca/14_4/index.html.

Eno, Brian. 1996. “Evolving Metaphors, in My Opinion, Is What Artists Do.” In Motion Magazine, June 8, 1996. https://inmotionmagazine.com/eno1.html.

Nierhaus, Gerhard. 2009. Algorithmic Composition: Paradigms of Automated Music Generation. New York: Springer.

Parviainen, Tero. “How Generative Music Works: A Perspective.” http://teropa.info/loop/#/title.

Cyber Security - Location and Context-Awareness.

 

Context-Aware IMS Solutions came about with the development in the number of mobile devices and mobile paradigms. It became a necessity for IMSs to consider the location of users [53]. 

1. Location-Based Services.

Location-Based Services (LBSs) are systems that deliver information based on the location of people or devices [54]. 

Some LBSs go a step further in delivering valuable services by using the users' location to infer additional information about the area. 

To get there, existing IMSs track users' whereabouts so that they may be taken into account during management operations. 

An LBS may be either person-oriented or device-oriented, depending on the emphasis of services. 

2. Scenarios for Context-Aware Applications. 

This section depicts many situations in which the PBM paradigm aids IMSs in the processing and protection of information, as well as the configuration and behavior management of systems. 

3. Proposals for Context-Awareness

Many context-aware services have been suggested in recent years in attempt to make life simpler. Despite the fact that the word "context" was coined in 1994, the first context-aware solution in the literature was offered in 1991 [69]. 

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read and learn more Technology and Engineering here.

You may also want to read and learn more Cyber Security Systems here.

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