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  • Writer's pictureCallie Davis

You Think Your New Song is the Next Big Hit? With this AI Music Analysis, You’ll Know for Sure…


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AI Music Analysis

Have you happened to release a song but wasn't sure if it's a HIT song? Now you can be 97% sure.


The world of music is constantly evolving, with artists and producers always on the lookout for the next big hit. Labels, artists, and producers are constantly seeking ways to anticipate the next big sensation in order to make strategic decisions and achieve commercial success.

Thanks to advances in neurophysiology and machine learning, accurately predicting hit songs is no longer just a distant dream. Now, with this new life-changing technology, upcoming artists have the ability to know if they’re music will be the next big hit.

Creating popular songs has traditionally relied on a combination of talent, creativity, and industry knowledge, however a recent development has been discovered that might revolutionize the process, using solely artificial intelligence mainly focusing on accuracy.


This groundbreaking research and its potential impact on the music industry, hopefully helping independent artists thrive in the music business.


The Study:

Researchers from Claremont Graduate University conducted a study in which they used AI algorithms to analyze listeners' brain waves while they listened to a vast database of songs, both popular and relatively unknown. By using this comprehensive machine learning technique, the AI system was then able to identify specific patterns and correlations between brain wave data and the listeners' preference for certain songs. The goal was to predict future hit songs by identifying these patterns in the brain wave data.

By utilizing demographic data and preferences, scientists were able to measure the participants’ neurophysiologic responses to specific songs, allowing them to determine which songs would be most popular among the group. This process is known as ‘neuroforecasting’.

Specifically, they observed participants' brain activity through functional magnetic resonance imaging (fMRI) scans, and the researchers identified specific brain regions associated with music processing. They discovered that certain neural patterns corresponded to different musical features such as melody, rhythm, and harmony. These findings opened up possibilities to determine listeners' preferences based on neural responses.

Within the first minute of a song, this new technology is able to indicate a hit song with about an 82% success rate, displaying that the beginning of songs are the most important when discovering their potential popularity.


Implications of AI MUSIC on the Music Industry:

This new technology entering the music industry can completely transform the way artists write their music, in both positive and negative ways.

For example, artists won’t want to promote a new song unless they know 100% that it will be popular and going to hit the charts, since they now have the technology to find out.

Although this technology can be extremely useful, it can potentially damage the creativity process of writing music and impede on individuality.

However, the implications of this research for the music industry are immense. Traditionally, record executives rely on subjective opinions and personal preferences when deciding which songs to promote. With the introduction of AI technology that can accurately predict hit songs, the way the industry operates could change drastically as the success rate of signing new talent and producing hit songs could potentially skyrocket.

This new technology can tremendously impact record labels profits, revolutionizing the entire industry.

Promotion budgets, which are the highest record labels spend, will no longer be invested in a song that doesn’t have the right hit criteria.

This technology can also impact the independent music artists industry and artists with low promotional budgets, no more throwing money on the wrong song.



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AI Music


Enhancing the Creative Process:

While some may argue that relying on AI technology to determine hit songs might undermine the creativity and artistic vision behind music, it is crucial to note that AI is being used as a tool, not as a replacement.

Since we are in such early stages of AI, many people don’t know its capabilities, therefore it is crucial that people keep their reliability on it to a minimum. There is so much unknown about the technology, that we don’t even know if it's 100% credible all the time.

However, looking on the bright side, by analyzing brain wave data, AI can help identify patterns that resonate with listeners, giving artists and producers a better understanding of what works and what doesn't. This can greatly enhance the creative process, allowing artists to make more informed decisions while maintaining their artistic integrity.

Also, they made sure to test its validity in the study. The predictions made by the algorithm were subsequently validated against real-world data. Surprisingly, the accuracy of the model in recognizing hit songs was found to be remarkably high, consistently outperforming human predictions.


Music Discovery and Personalized Playlists:

In addition to predicting future hit songs, AI's ability to analyze brain waves could also revolutionize music discovery and personalized playlists. People are always craving new music, however they usually prefer songs similar to those they already know. By understanding individual listeners' preferences and analyzing their brain wave data, AI algorithms could curate personalized playlists tailored to each person's unique tastes.

For example, AI algorithms are being deployed by streaming platforms like Spotify, Apple Music, and Pandora to deliver highly personalized music recommendations to listeners. By analyzing vast amounts of user data, AI can understand users' musical preferences, behaviors, and patterns, helping platforms curate playlists and suggest new artists or songs that align with individual tastes.

This personalized discovery experience not only enhances user engagement but also provides valuable exposure for lesser-known artists. This could lead to a more immersive and personalized music experience for listeners, opening doors to a vast array of emerging artists who may have otherwise gone unnoticed.


Ethical Considerations:

While the accuracy of AI in predicting hit songs is impressive, ethical considerations must be taken into account. The potential for AI to manipulate and exploit listeners' preferences for the sake of commercial success raises concerns about authenticity and artistic freedom.

It is essential to strike a balance between leveraging AI technology for its benefits and ensuring that artists' creativity is not compromised. Moreover, privacy concerns need to be addressed to ensure that listeners' brain wave data is collected and used ethically and responsibly.

All in all, employing ethics is critical to make sure there is integrity and fairness when using AI in the music industry.



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The Future Of Music


Final Thoughts:

When looking at this from a progressive perspective, this is an extremely exciting development in the music industry that can create a more personalized experience for listeners and a more successful career for musical artists, along with record labels.

By utilizing AI algorithms and analyzing brain wave data, researchers have achieved an astonishing 97% accuracy in predicting future hit songs.

While AI revolutionizes music discovery, there are also ethical concerns that need to be carefully recognized and addressed. As the music industry evolves, it is essential to strike a balance between technology and artistic expression, ensuring that AI serves as a tool to amplify creativity rather than stifling it and decreasing the individuality of artists.

The promising results obtained through the integration of neurophysiology insights and machine learning algorithms indicate that we are only scratching the surface of what AI can achieve in the realm of art and creativity.



Source Research:

https://www.frontiersin.org/articles/10.3389/frai.2023.1154663/full

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