The most crucial definition is musical work–the selection and arrangement of notes, sounds, and beats (divisions of time) that can be sung, put to words, played on an instrument or sampled digitally. These components can be combined and layered in varying intensities over time within a rhythm, making music creation such a complex and amazing art form.
When a human selects or arranges notes, sounds, and beats, they are the essential composer of that work. However, if something else, like a machine, selects or arranges them, it is the essential composer.
We fully support musicians who use AI in ways not covered by the standard or who find it challenging to agree to it for various reasons.
For instance, some musicians use AI to generate intentionally curated, composited, and recombined tracks to form remarkable and innovative tracks, which may not fit well with the VerifiedHuman™ label. However, they might find it useful for other works created using traditional methods.
In this way, we aim to be helpful and encouraging to all musicians everywhere.
There are two common cases we want to address in this foreword.
i. AI Assistance in Composing, Editing, or Scratching Out Musical Work
It's commonplace for people to use AI to assist in composing, editing, or scratching out samples of musical tracks. These processes sometimes include removing unwanted sounds, frequencies, or noise from existing tracks, or correcting/adjusting variants like pitch, texture, tone, or timbre. The VerifiedHuman™ Standard supports the assistance of AI in composing, editing, or scratching out tracks. It also supports generative AI for creating sounds to be incorporated into the musical work provided the complete and final work is essentially distinctive from any individually generated element (See the Five-Note/Five-Beat Principle below). When generative AI heavily influences musical work, we suggest using the VerifiedHuman™ label with the AIA (AI–Assisted) tag: VerifiedHuman™AIA. We leave it to the musician's discretion when the tag is helpful.
ii. Using Content of Unknown Origin in Musical Work
Sometimes, writers use sounds, tracks, loops, rhythms, and samples where the source material is of unknown origin. In other cases, the musician may suspect that AI has generated some of the material they are using, but the origin, time, place, purpose, etc., are unknown or uncertain. In such cases, we encourage writers to use the VerifiedHuman™ label with an AIU (AI–Unknown) tag: VerifiedHuman™AIU. We leave it to the musician's discretion when the tag is helpful.
In the spirit of this standard remains the question: Did you compose it, or did AI compose it? In both cases above, we appeal to each musician's understanding of their intention to create and present musical work as their own.
Here are commonly accepted ways AI assists in composing and enjoying music.
Curation through popular streaming services like Apple Music, Spotify, and Pandora uses AI to create personalized playlists based on users' preferences.
Algorithmic technology is used to add a vast range of effects to recordings and live performances, making digital effects an integral part of musical production.
AI-assisted mixing and mastering is a relatively new concept. By leveraging the power of AI, music can be made more powerful and enjoyable.
More and more musicians are using composition software to explore innovative forms of cross-genre music, which is becoming more widely accepted.
Using AI to generate writing prompts for songwriters is a commonly accepted practice that helps them explore new ideas and create music that resonates with their audience.
COMMON USES OF AI IN MUSIC
Numerous questions, similar to the one below, call for musicians to rely on their independent interpretation and be guided by their personal values.
Q: Can I use AI-generated music as a starting point for my own version of the song?
As a musician, it's important to exercise caution when leveraging generative AI in music composition. You should determine the extent to which you want to rely on the AI-generated music and how much you want to modify it to create your unique version. It can be challenging to differentiate between your own creativity and the algorithmic power of AI.
When it comes to composing music, simply copying and pasting entire musical sequences is not true composing. It means that you are replicating a composition that has already been created by someone or something else. As a general rule, if AI selects five (5) specific notes and puts them together into a sequence, then the AI has written that sequence. The same principle applies to unique collections of percussion. Beats are merely marks in time, but if AI selects five distinctive percussive sounds and puts them together in a sequence, then AI has written that rhythm. Five is not a magical number, but it's considered enough to be a considerable sequence. The main idea behind this principle is to ask the question, "Did you create it or did AI?" This pertains to all compositions, no matter how many notes, sounds, or beats are involved.
THE FIVE-NOTE PRINCIPLE
Musicians create compositions by arranging notes, sounds, and beats, forming melodies, rhythms, and songs.
The fundamental question that establishes the essential composition of a musical work is Who or what composed it. This applies to entire scores, songs, or even smaller sequences of musical work.
Music creation is based on two primary elements – the note and the beat.
Compositions come to life when notes and beats are arranged in a way that produces audible music. This can be achieved through singing, playing an instrument, sampling, or layering sounds.
ASSUMPTION OF HUMAN COMPOSITION
If a human puts notes and beats together to produce music that can be experienced meaningfully, then that human is the essential composer.
ESSENTIAL HUMAN COMPOSITION
Other definitions of words and ideas related to the Standard for Writers are here.
Artificial Intelligence (AI)
A field that combines computer science and robust datasets to enable problem-solving. It also encompasses machine learning and deep learning sub-fields, frequently mentioned in conjunction with artificial intelligence. These disciplines comprise AI algorithms that seek to create expert systems that make predictions or classifications based on input data.*
AI-Affective Music Generation (AI-AMG)
An interdisciplinary field that requires knowledge of artificial intelligence, music theory, and principles of music composition, as well as the fundamentals of affective science.*****
AI language modeling (or Large Language Modeling (LLM))
Systems that can generate natural language texts from large amounts of data. Large language models use deep neural networks, such as transformers, to learn from billions or trillions of words and to produce texts on any topic or domain. Large language models can also perform various natural language tasks, such as classification, summarization, translation, generation, and dialogue. Some examples of large language models are GPT-3, BERT, XLNet, and EleutherAI.**.
A repeating measure of time
Generating new musical compositions based on existing datasets of music using AI learning and training
Emotion Evaluation/Emotion Targeting
Training AI systems to create affective music to inspire a particular emotional response in humans
A way of understanding or explaining the meaning of something
A musical sound representing a pitch in a duration of time
Personal, original idea
An idea representing a specific human being's unique insight or experience in the world
Capturing sounds that can be played back
Rule-based composition (AI)
Generating music with AI by defining theory-based parameters such as specific progressions of chords, lines of melody, or rhythms within which AI can create musical work
Refers to work in unfinished draft form
Scratch or scratch track
Refers to making a rough track or rough version of a sequence to use as a working reference
A larger collection of music arranged thematically
A part of a song or musical track containing notes, beats, or other sounds
A short poem or series of words set to music and meant to be sung or recited.
Principles or standards of behavior
Relating to principles, values, or ethical assumptions that motivate human behavior
*IBM | What is artificial intelligence (AI)?
**Microsoft | Learn | LLM
****McKinsey & Co. | What is generative AI?
*****AI-Based Affective Music Generation Systems: A Review of Methods, and Challenges | ADYASHA DASH and KAT R. AGRES
This document provides clarity on terms in the standard and their meanings. It also helps musicians understand the motivation behind the standard.
What's in this document?
Here are definitions of words and ideas used in the Standard for Musicians.
A specific definition of human behavior
A person who composes (or plays/performs) musical work
Present, share, or show work with others
The selection and arrangement of notes, sounds, and beats. Musical compositions that can be sung, put to words, played, or sampled by putting notes and beats together
A group of people working together
A work or invention that is the result of creativity, such as a manuscript or a design, to which one has rights and for which one may apply for a patent, copyright, trademark, etc.***
The fundamental elements or characteristics of something
Essential elements in music
Notes, sounds, and beats put together to form musical compositions
Selecting or arranging the essential components of music–notes, sounds, and beats to create musical work (see Essential Composition below)
Noun: a human person; adjective: from a human person
Shorter definition–machines that create novel (new) content | Longer definition–generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos.****
"Systems that act like humans"*
Other generative processes
Other processes involving AI or machine learning to create novel (new) content
*IBM: What is artificial intelligence (AI)?
**Microsoft | Learn | LLM
DEFINITIONS IN THE STANDARD