Music Evolution: Exploring How Technology Has Changed Music - Research paper

Introduction

Music is an essential human skill and a productive process that echo intellectual abilities. It promotes social welfare through enabling interaction, sense, and imaginations of potentials trying it to our societal instincts. As humanity evolves, music has also been changing from the traditional methods of composition, performance, and analysis to modern methods. Technological advancements have played a significant role in music evolution. Computers and other artificial intelligence (AI) equipment have helped in the automation of music composition, hence rising talent records economically. Machine learning is a subset of AI that has helped in shaping music research and development. This study will effectively analyze how computer technology can be upgraded to interpret music efficiently. It will also focus on the exploration of how music research has been impacted by artificial intelligence.

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Studying Humans

Humans are diverse genetically and culturally. Music is a language that conveys emotions to humankind. Music began many years ago. The early man was able to use vocals and control pitch just like the modern man. Musical instruments also began to evolve from the early man's era. The artistic tools used by the ancestors were not durable because they were made from soft materials like reeds and wood. However, some wind musical instruments like the bone pipes lasted longer. The ancestors used to make their music in their caves which acted as resonators for the sound.

The early man used music for different purposes. It was used for dancing, entertainment, communication, ritual, and bringing people together. The most crucial aspect of music is the fact that it brings people together. Music leads to bonding not only the family but the society as well. Modern music is mostly made by the generation through artificial intelligence.

Machine Learning

Modern musicians rely on machines to compose either a specific part or the entire music with some editing. For example, Landr is an online platform that offers an automatic device that can master particular audio records within a short time created by machine learning. Album mastering is also an online platform which assists users in uploading a sequence of songs to make it a coherent sounding end production. Mastering is a crucial component in music composition that used to require manual work. These online platforms express time and economical options to outdated workflows that contain various physical tasks and costs for expert services (Miranda 165).

The machine learning is accomplished by feeding the AI systems with a vast quantity of data. Typically, the data is by this time available in large capacities, but it lacks arrangement. Organizing and creating the material used cost-effectively is one of the present responsibilities in exploration. Through the collection of data through various amounts of musical flow can be a challenging task. For instance, creating an AI that can entirely modify and combine specific sound sources like the songs of various musical devices would entail admission to the raw and unraveled songs within song recordings (Miranda 72). However, the task is that the collaborating material is commonly protected by law.

For machine learning to efficiently work in automatic music composition, an algorithm is required for the production of music sheets or sound synthesis. Algorithm models are a set of rules that have been used in music composition for quite a long time. The technique of using algorithms to create music is known as the algorithm composition. Several algorithm models are used in musical composing.

• Knowledge-based models. The algorithms in this model remove the aesthetic code of genre first. It then uses the code for producing a new composition. The unique piece will be similar to the previous form.
• Learning-based models, the algorithms first learn the genre of music from the provided parts. It then produces pieces based on acquired styles.
• Mathematical models, the models are based on the mathematical equations and variables which uses statistical processes.
• Grammar methods, the algorithm used in this model produces musical pieces by a set of rules. The rules define macro-level composition instead of single notes.
• Hybrid systems, thesis systems combine any combination of the models for optimal performance. The selection depends on the algorithm which can control the complexity involved. It uses the superior features from single methods the get the best results.

Some machine learning techniques have been implemented for music analysis, retrieval, and compositions. The artificial neural network was taught to categorize melodic chords in different classifications: Major, dominant seventh, and minor or diminished seventh. After training, the interior arrangement of the system is analyzed to define the representation that the system is using to categorize music chords. The researchers established that the primary layer assembly weights in the networks transformed the local images of the input musical notes to the circulated ones. They can be defined as the circles of major thirds and seconds. It illustrates a likely impact of the artificial neural networks to the cognitive innovation of new procedures of demonstration in structures that can achieve intellectual tasks (Yaremchuk et al. 26). Composing music using the recurrent neural network (RNN) does not necessarily need a musical expert. Nonexpert can generate a piece of decent and good quality music using RNN. The RNN can create music that can be compared to the music composed by humans. The music created has both harmony and melody (Rather, Mohiuddin, et al. 3238). It is easy to predict the next note in a sequence. The machine model is given an existing musical data, and then it trains itself using the data. Once the machine is conversant with the musical data, it learns the patterns in music that humans enjoy. The model of image development software is intermittently released for others to use on the art-making instrument with variable degrees of commercialization, interface growth, and source code accessibility (Romero 26). The model can now generate the music that humans can enjoy. The music created is harmonious and pleasant to hear. These are some of the current imperative systems of machine learning in music production.

The biaxial recurrent neural network uses deep neural networks for prediction. It employs more than one node and layer for learning and predicting. They use Long Short-Term Memory (LSTM) approach in dealing with short term memory problem.

MarkovComposer uses the second order Markov chain in a composition process. The two previous notes determine the next one. The pitch and spacing between the notes are also stored in the Markov chain.

In RNNs for folk music generation, a system is trained on 1180 tunes in ABC format from a collection published in 1778.

MusicComposer provides stochastic based music composition and machine codes. The system is trained on 8-measure fragments extracted from an online collection of classical MIDI music.

DopeLearning uses RankSVM algorithm and a deep neural network model for retrieval.

In Modeling/generating polyphonic, the polyphonic are produced by systems trained on various collections of MIDI music using RNN-RBM which is an energy-based model for an accurate estimate of temporal classifications.

GRUV uses the RNN for generating music the project is based on Python language.

DeepAutoController builds deep graphical models. It provides details about code layer of a deep autoencoder which helps in the creation of a new sound.

In Irish folk music generation, the system trained itself on 23,962 tunes in ABC format from an online collection.

Synthesizing where the digital audio with RNNs, in which a system is trained on digital music audio of the group Madeon.

Once the data is extracted from these sources, feature classifications and number depends on the process and model that have been used for extraction. For new instances, the models are parsed for a given context. It is then compared as a motif in the tree and chooses the symbol for prediction probabilities if it finds the match. However, if the counterpart is not found, it removes the leftmost symbol and goes back to the previous steps. The step iterates and produces a sequence of symbols which should correspond to a new message from the source.

Artificial Intelligence

Since the development of digital computers, it has been demonstrated that they can be programmed to perform complex tasks. After any invention of new technology, its first use is usually test-scoring. For example, duplicate the existent functions to demonstrate the technology's usefulness. Getting rid of non-performing but desired services (Anagnostopoulou, Ferrand, et al. 37). Also eliminate the previous tedious, dangerous, and undesirable methods of performing a function.

In music, a high volume of work completed by the computer has involved the first way of skill to simulate human beings. They have upgraded the tedious activities in music composition processes such as technical instrumental practice and music copying. Computer technologies have also improved the speed and quality of music copying. There have been efforts to simulate other features of human music preparation such as creating, practicing, understanding, inventing, and listening (Miranda 147). However, they have not been quite successful.

AI has been critical in the transformation of information technology. Artists and composers have been using different statistical methods in making music. Algorithms have been deployed in music which helps in making cumbersome and time-consuming tasks more efficient. Big companies like Google have embraced the idea of AI. For example, Google formed Magenta which is a plan aimed at helping musicians in writing songs. Automatically estimating the first beats is useful for studying the links between different elements of the same piece of music. Dancers, on the other hand, can perform similar or contemporary steps by counting the beats of the measure. Knowing the first beats will make it possible to synchronize two musical audio signals with a virtual dancer, a drum machine, or a play fo light (Goto 320).

Equally, Sony's Computer Science Labs made an AI system named Flow Machines which produces a tune after learning a variety of styles from a variety of songs. Verbalizer is a program that was created by David Bowie and Ty Roberts. It rearranges words into combinations. There are also other available programs on websites such as Jukedeck and Amper Music which lets artists experiment, choose categories, and attitudes before leaving the computer to work on the rest.

AIVA is an artificial intelligence system that composes classical pieces. It learns the skill of music composition by evaluation of an extensive collection of music panels, written by great music musicians to generate a mathematical model illustration of what the piece is. It attains this by applying the deep learning algorithm which is stirred by the neural networks of the mind. The writing is then articulated for actual artists to play (Gentsch and Pete 54). AIVA was the first AI to acquire the global position of initiator and the technology's masterpieces that have been presented in movie sounds, sports, and advertisements.

Streaming services prefer AI as they do not have to pay third-party royalty holders. However, the lawful possession of AI composition is an indefinite question, and it could hurt the careers of music composers in the future. It is necessary to establish whether the musicians using AI platforms are the writers of the compositions to avoid any legal obstacles.

There is also a little doubt on the quality of AI music programs. Various artist...