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The Unfair Critique: Bashing AI-Generated Music

This section covers topics such as songwriting, composition, music production, and the creative process, as well as what is currently happening in the music scene, medical research, etc.


It is important to recognize that the statements in this section are solely my opinions and should not be taken as fact. It is important to do your own research and make decisions based on facts rather than opinion. It is also important to remember that opinions can change over time and should not be taken as concrete.

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In recent times, there has been a growing trend to disparage Artificial Intelligence (AI) generated music as inferior or unoriginal. This criticism often stems from claims that AI algorithms simply copy existing compositions without adding any real creative value. However, this argument is not only unfair but also neglects the fact that humans themselves have long relied on existing musical elements in their own works.


Several composers have created melodies that bear similarities to those of Wolfgang Amadeus Mozart, either through stylistic influence or intentional homage. Here are a few notable examples:

     1.     Joseph Haydn: Often considered a mentor to Mozart, Haydn's works, particularly his symphonies and string quartets, share harmonic structures and melodic characteristics with Mozart's compositions.

     2.     Ludwig van Beethoven: Early works of Beethoven, particularly his piano sonatas and string quartets, show the influence of Mozart’s style. The themes and melodic lines can sometimes resemble those found in Mozart’s music.

     3.     Franz Schubert: Schubert was known for his lyrical melodies, which can remind listeners of Mozart's lyrical style, especially in his lieder and symphonic works.

     4.     Carl Maria von Weber: Some of Weber’s operatic melodies, notably in works like Der Freischütz, can evoke similarities to Mozart’s melodic and harmonic language.

     5.     Giuseppe Verdi: Certain melodic lines in Verdi's operas, while more dramatic, can bear resemblance to Mozart’s memorable and singable tunes.

     6.     Another example can be found in Carl Orff's choral masterpiece, "Carmina Burana” in the 1930’s. This work is heavily influenced by medieval Latin songs and Gregorian chants. Orff skillfully wove these ancient musical threads together, creating a unique tapestry of sound that has become an integral part of classical music repertoire. Rather than being criticized for borrowing from others, Orff's creative synthesis earned him widespread acclaim.


Humans have long relied on the works of other artists to inspire their own compositions. This process, known as intertextuality, acknowledges that all art builds upon what came before it. Musicians often draw inspiration from past masters, incorporating elements into their own creations while adding new layers of meaning or interpretation.


Here are a few more examples of musical compositions from the classical era borrowed from past composers:  


As I mentioned earlier, Beethoven wasn't the only one to borrow from Mozart. Many composers, including Haydn, Schubert, and Brahms, drew inspiration from Mozart's works.


Tchaikovsky: In his famous ballet "Swan Lake", Tchaikovsky used melodic fragments and themes inspired by traditional Russian folk songs and dances. He also borrowed from Italian opera composer Giuseppe Verdi in some passages.


Debussy: The French Impressionist composer often incorporated elements of Javanese gamelan music into his compositions, such as in his work "Prelude to the Afternoon of a Faun". This was likely due to Debussy's fascination with non-Western musical traditions.


Stravinsky: Igor Stravinsky, known for his iconic ballets like "The Rite of Spring" and "Petroushka", drew inspiration from various sources, including African rhythms (in "Rite") and Balkan folk melodies (in "Elegy for Mihailo Colomanescu").


Shostakovich: Dmitri Shostakovich frequently referenced Russian folk songs and classical composers like Tchaikovsky, Mussorgsky, and Rimsky-Korsakov in his works.


Bartók: The Hungarian composer Béla Bartók was renowned for collecting and transcribing traditional folk music from Eastern Europe and the Middle East. He often incorporated these elements into his own compositions.


Some notable examples of specific pieces that demonstrate this trend include:


Mozart's String Quartet No. 19, which features a theme inspired by Haydn's Cello Concerto


Brahms' Symphony No. 3, which quotes themes from Bach's Mass in B minor


Ravel's "Boléro", which draws inspiration from Spanish flamenco rhythms and melodies


Shostakovich's Piano Quintet, which references Russian folk songs and classical composers like Tchaikovsky


Keep in mind that borrowing or referencing other works is not unique to AI-generated music; it has been an integral part of artistic expression across various mediums for centuries!


The practice of incorporating borrowed elements into one's own work continues well beyond the Classical Era. Here are some examples from the Modern Era:


Steve Reich: The American composer often incorporates melodic fragments and rhythmic patterns inspired by African-American spirituals and jazz standards (e.g., "Music for 18 Musicians") or Minimalist compositions that reference earlier styles.


Philip Glass: Another prominent figure in the Minimalist movement, Glass has drawn inspiration from a variety of sources, including Indian ragas (e.g., his opera "Satyagraha"), medieval chant melodies, and even commercial pop songs.


Brian Eno: As a pioneering electronic music artist, Eno frequently incorporated elements from various genres, such as ambient textures reminiscent of Erik Satie's piano pieces or distorted rhythms evoking the sounds of industrial music.


Radiohead: The influential British rock band often draws sonic inspiration from diverse sources, including Krautrock legends like Can and Neu!, avant-garde composers like Karlheinz Stockhausen, and traditional folk music from around the world.


Keep in mind that this practice continues today, as many contemporary artists engage with diverse musical traditions and styles.


In contrast, AI algorithms are designed to analyze vast amounts of existing data, identifying patterns and relationships between different pieces of music. They then generate original compositions by combining these insights with algorithmic rules and constraints. While this may seem like mere imitation, AI systems can also recognize subtle connections between melodies, harmonies, and rhythms that might elude human composers.


Moreover, AI-generated music is not simply a copy-paste job; the process involves complex computational processes and artistic decisions made by developers. The output is shaped by the machine's understanding of musical structures, cultural context, and emotional resonance. This synergy results in novel sounds that often defy categorization as purely "original" or “derivative".


It's time to reevaluate our approach to evaluating AI-generated music. Rather than dismissing it out-of-hand due to perceived similarities with existing compositions, we should acknowledge the creative potential hidden within these algorithmic creations. By recognizing the interplay between innovation and homage in both human and artificial intelligence-driven artistry, we can foster more nuanced discussions about the role of creativity in music.


As we continue to exist, the incorporation of borrowed elements into one's own music has become even more complex and widespread due to advancements in technology, particularly artificial intelligence (AI). Today, we see numerous instances where musicians:


Collaborate across genres: Artists blend different styles, such as electronic producers incorporating traditional folk melodies or metal bands incorporating jazz harmonies.


Draw from global inspirations: Musicians draw upon various cultural heritages, like African rhythms in electronic dance music, Indian ragas in ambient compositions, or Latin American influences in pop songs.


Incorporate sampling and interpolation: Many artists use samples, interpolations, or reworked melodic fragments from other sources, often with the aid of AI-assisted tools.


Access vast musical archives: AI-powered music databases enable creators to draw inspiration from a massive repository of melodies, harmonies, and rhythms.


Experiment with novel sounds: AI-driven sound design tools allow producers to generate unique textures and timbres by combining elements from diverse genres or styles.


These technological advancements have blurred traditional boundaries between different musical traditions, allowing artists to explore a vast array of influences and inspirations. As this process continues, we can expect even more innovative fusions and reworkings of familiar melodies, harmonies, and rhythms across genres.


Key Points:


          AI Music’s Originality: AI-generated music, like human music, can be original despite drawing inspiration from past works.

          Creative Process: Musicians, human or AI, draw inspiration from diverse sources to create new and unique music.

          Embracing Diversity: Appreciating the artistic value in both human and machine-created music unlocks new possibilities for musical expression.


For further reading: Music and the Human Brain, Second Edition, by Eliasar A. Simon, MD, on Amazon.


For further music listening: Salidona.com

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Eliasar A. Simon, M.D.