How can you evaluate the quality of AI-generated music?

DQ in AI-generated music values the complexity. While traditional music relies on the nuanced experiences and creativity of human artists, AI-generated compositions challenge our understanding of these inherent qualities. Creativity, coherence, and style, traditionally seen as human-centric attributes, are now being replicated by algorithms, prompting us to reconsider what authenticity means in art. Furthermore, as AI caters to diverse audiences, it also raises questions about the universality of music. Do AI compositions lack the soul and story behind human-created music, or are they forging a new narrative in the ever-evolving tapestry of sound? The intersection of technology and artistry is undoubtedly reshaping the musical landscape.

While we can have a very unique range of AI generated music, in order for us to be able to create more “human-like” music in terms of creativity, it’s important to make sure that AI models are trained adequately. What is meant by this is that it is important to have models that are essentially trained to better understand the “emotional” context of a piece of music. This would then result in AI generated music that infuses this emotion and therefore mimicking human-like responses to different moods and themes.

This is a tricky question because it’s based on the subjective interpretation of the listener and is not easily quantified. The quality of music extends beyond technical attributes, striking a personal chord with every listener. Even a technically perfect piece composed by an AI may fail to emotionally engage a listener, while a simple, imperfect melody could profoundly move them. The essence of music's quality resides in the subjective experience and personal connection of the listener. While an AI can replicate styles and structures, it lacks the capacity to "feel" music or comprehend its potential emotional impact on each listener. Quality is a relative term and is, therefore, subject to the interpretation of each listener. </dave>

Absolutely, assessing AI-made music can be looked at in a few ways. Think of it like this: Music made by AI is about setting up patterns that turn into sounds which are nice to hear. People usually enjoy music that feels human, that connects with them on a personal level. So, music is pretty much based on numbers and patterns that can be figured out. So, we should be able to use math to check if the music does what it's supposed to do. We should also be able to see if the music can make us feel something special. And, of course, we can look at how many people like and listen to the music. In short, to know if AI-created music is good, we just need to see if it hits the right notes, feels right, and is liked by many. That's about it!

Music evaluation methods are divided into subjective and objective. Subjective methods rely on human judgment, including music listening tests and visual analysis. Listening tests involve feedback from listeners on aspects like creativity and harmony, while visual analysis examines music representations to assess musicality. Objective methods use metrics to analyze melodies, focusing on the generative model and music theory. Model-based metrics include training loss and chord prediction accuracy, while music domain metrics assess pitch, rhythm, and style. A combined approach uses both methods for a comprehensive assessment, correlating objective metrics with subjective judgments for a well-rounded understanding of music quality.

AI algorithms have demonstrated a capacity to generate music across a spectrum of genres, from the structured cadences of classical compositions to improvisational jazz and pop songs. The technical proficiency of AI-generated music is often remarkable. The ability of these programs to analyze and replicate complex patterns and structures in music is a testament to the advances in the field. The compositions can be intricate and technically sound, sometimes indistinguishable to the untrained ear from human-created music. Yet, the nuances of emotion, the subtle inflections that give music its depth and character, are often missing or, at best, simulated especially when it comes to vocals.

Although I am yet to hear an AI-generated melody, I believe there will be different evaluation criteria for different types of music. 1. Format: Instrumental, Vocals, Background score. 2. Genre: Pop, Blues, Country, hip-hop, Jazz etc 3. Authenticity of Instruments: Strings, percussion, drums, flute etc. 4. Emotion/Feelings: Happy, sad, anger, surprised etc. 5. Different music cultures of the world: Chinese guzheng music, Indian raga music, Tibetan chants, etc. For vocals, we can collect survey data to assess the authenticity of AI voices to human voices. For the authenticity of instruments or cultural references, I believe we have a long way to go as there are many intricacies and nuances. I'd be happy to learn different perspectives.

Music is an area of direct return from AI as well as AI applied in music bringing solutions for other areas. There is the creation for the first time with little or no “data”. Then there is the past experience used to inspire as well as to contribute to the creation of music. Music can be created standalone or in collaboration as well as extended and revised either alone or in collaboration. Music can come from other sources as well as from the page or in combination. There is also the personal choice or personal profile aspects of music. Benchmark testing (the data and the learning by algorithms and the performance to baselines) is also part of the music development. The role of AI in music is set to have its challenges and wider returns.