The Voice of a Musician in the Machine Age
In a not too distant future, we see robot musicians topping the charts in all genres. Music awards are no longer given to creative artists, but machines and their makers who create smash hit after smash hit. These producer robots listen to millions of songs a minute, analyzing music trends endlessly to stay ten steps ahead of the game. Just like we’ve seen before, human artists have been eclipsed by a new generation of machines, who garner the popularity of the general public, losing their jobs and livelihoods. When you think of Artificial Intelligence (AI), is this what comes to mind?
There is some alarm that surrounds the adoption of AI, stemming predominantly from the controversy behind this technology. While some are strong supporters of its benefits to our productivity, other paint it as our impending doom . Coupled with a general lack of understanding regarding this issue , there exists a strong sense of unease when discussing this topic. In actual fact, like all other technologies, AI is just a tool – an instrument whose product depends on the person using it. There is little reason to fear its arrival and adoption.
Artificial intelligence has become a buzzword in our current day as it continues to create significant advances in multiple industries. This technology has proven to show the ability to significantly improve current work processes and elevate productivity, and often times is even touted as the fourth Industrial Revolution . While the Music industry is not the current hotbed for applications of AI, we are beginning to see its introduction into the music scene, from studio sessions to live performances. As we prepare for its fated arrival, the big question arises: how can we prepare for a new Music Age?
Essentially, AI is simulated intelligence by machines that have been programmed to mimic human action and rational thought. It is a field of data science where computer programs use data to identify patterns and trends to gain insight and develop solutions to problems. Conceptually, AI is engineered to learn from past experiences to make better decisions for future situations. The technology relies on algorithms that use historical data for training, consuming human-like inputs such as images, language, and actions for inspection and analysis. After the learning process, algorithms become capable of making predictions and suggestions for rational decision-making at a significant level of accuracy. This makes AI very useful in automating tasks and forecasting events. With recent advances, AI is able to solve larger challenges and answer tougher questions, making headway for widespread use in many area such as smart homes, automobiles, and e-commerce .
Indeed, the decision-making power of AI and the rate of its growth create anxiety over the future of the job market. Adoption of a cost-effective technology indicates the replacement of labor with capital, signaling that certain jobs in today’s industry will not exist in the world of tomorrow. However, this marks a surge in creative freedom for human workers, who now not only possess better gadgets at their disposal but also are liberated of repetitive, menial tasks and can focus their efforts on higher order thinking and creation. Just as electricity revolutionized the way we work today, AI will change the way we work tomorrow. There has never been a time of more opportunity for artists to develop their music and find success in their field.
With the ability to automate tasks and do analysis, AI has large potential to improve workflows and create fundamental shifts in music making, performance, and distribution. By removing the burden of routine tasks, musicians can concentrate efforts on the creative aspects of their craft. Moreover, it creates new tools for musicians to reinvent their workflow and challenge the perceptions of what is possible with audio. The possibilities for new ways to create and distribute music is ever-increasing, enabling musicians to find their market, solidify their style and achieve greater success on the business end.
In the Studio
Artificial intelligence can help to eliminate various processes and inspire music producers and engineers. The Flow Machines Project helps artists overcome writer’s block by suggesting chord variations based on certain progressions, melodic sequences, and rhythm . Technologies like Jukedeck even create sample background tracks for songs, making music writing more accessible . During live recording, algorithms can automatically detect and remove click track and mic bleed from recorded tracks, simplifying the track cleaning process . Also, computer programs can identify preferences and design decisions to smoothen the workflow without compromising style and quality. Artificial intelligence enabled plugins for mixing and mastering music, such as the Track Assistant feature in Neutron 2 by Izotope that creates automatic presets for the engineer, setting auto-release, ratio, and attack on the compressor based on selected styles and intensity. Landr, a big data music mastering company, analyzes tens of thousands of songs to understand mastering decisions by engineers and has developed a mastering engine that automatically adjusts dynamics, bass levels, and compression of a track . In addition, search algorithms can analyze the current software that engineers use to recommend plugins and samples that may be of high interest, making software discovery more efficient. Music collaboration platforms with AI technology can perform evaluations of potentially successful collaborations between artists based on past hits, matching vocal ranges and musicality to produce suggestion lists and prompt effective partnerships for music.
On Stage
The benefits of AI extend to the live stage as well. Some musicians have begun experimenting with it in performances, such as A.M.I. that listens to the improvisation of a musician and mirrors notes on virtual instruments to build live electronic orchestration in real-time . In addition, based on venue size, structure, and layout, computer programs can use past performance setup datasets that calculate and determine the optimal setup of audio equipment in terms of position, direction, location, and volume. With this information, concert setup costs can be minimized while maximizing the audio quality for delivering playback to performers as well as live music to the audience. Similar concepts were implemented when algorithms were used to design the architecture of a concert hall in Hamburg, Germany, to suit orchestral music . Moreover, AI algorithms can assist in live sound mixing. By studying a sound engineer’s past shows, it can develop intuition based on an engineer’s preferences and automatically adjust sounds based the engineer’s mixing patterns, such as setting level thresholds and auto-balancing technology to deliver sound that fits the engineer’s style. Light shows are also a common theme in live concerts, adding to the overall experience. By studying relationship data between past performances and their respective light shows, AI models can recommend appropriate light displays to match the ambience and emotions depicted during music cues within the show, reflecting the right mood and visual environment. Lastly, AI technologies can act as an automatic fail-safe for technical faults which can be costly. During technical difficulties, diagnostics programs can immediately locate the source of error, visualize the fault and fix the issue present. Live diagnostics services can also be employed to check for smooth connections, log fault warnings, and prevent incidents from happening.
In the Market
Analytics tools built with AI technologies can also help musicians reach their intended market and target audience. Using past marketing campaigns and engagement, these algorithms can study an array of variables, such as distribution medium and selected marketing channels, time and date of album release, and type of content used for advertising to determine effectiveness and optimal method of delivering music with the highest projected outreach . With such tools, artists and artist managers are better able to identify what type of content to deploy, when to schedule music releases, who to market to, and how to shape their brand for commercial success .
Most applications of AI are built by musicians with the purpose of enabling musicians. Consequently, most of these products do not attempt to fully automate sound engineering tasks or replace humans involved in the music production process. Instead, they try to take advantage of the huge potential of algorithms to accelerate the music making process, making it more fun and taking sound processing a step further.
Artificial intelligence certainly does not concoct the hypothetical jobless dystopia that many people love to talk about . On the contrary, it creates greater potential for musicians to build their craft. It opens doors to new opportunities that test the limits of what is possible. What is critical for artists to be successful is to always be aware and continually learn how to apply technology to the music making process. Absorb what is useful. Discard what is not. Add what is uniquely your own. By harnessing the benefits of technology, artists can be more creative, efficient and effective in expressing their sound.
Endnotes:
Bradley. 2017. Facebook AI Creates Its Own Language In Creepy Preview Of Our Potential Future
https://www.forbes.com/sites/tonybradley/2017/07/31/facebook-ai-creates-its-own-language-in-creepy-preview-of-our-potential-future/#7e851a0b292c
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Shani. 2018. The Fourth Industrial Revolution Moves From Automated To Autonomous. https://www.forbes.com/sites/forbestechcouncil/2018/06/04/the-fourth-industrial-revolution-moves-from-automated-to-autonomous/#311f1edc1e1e
Adams. 2017. 10 Powerful Examples Of Artificial Intelligence In Use Today. https://www.forbes.com/sites/robertadams/2017/01/10/10-powerful-examples-of-artificial-intelligence-in-use-today/#44e07a3e420d
Jordan. 2017. Inside the Lab That’s Producing the First AI-Generated Pop Album.
https://www.seeker.com/tech/artificial-intelligence/inside-flow-machines-the-lab-thats-composing-the-first-ai-generated-pop-album
Fishwick. 2018. Robot rock: how AI singstars use machine learning to write harmonies
https://www.standard.co.uk/tech/jukedeck-maching-learning-ai-startup-music-a3779296.html
iZotope. 2018. Removing Click Track and Speaker Bleed with RX 6. https://www.izotope.com/en/learn/products/rx/removing-click-track-and-speaker-bleed-with-rx-6.html
Wichern. 2017. How Machine Learning in Neutron Elements Helps Make Mixing Creative.
https://www.izotope.com/en/blog/mixing/how-machine-learning-in-neutron-elements-track-assistant-helps-make-mixing-creative.html
Magee. 2015. LANDR Drums Up $6.2 Million To Master Music With Big Data. https://techcrunch.com/2015/07/07/landr-drums-up-6-2-million-to-master-music-with-big-data/
Kaulinyte. 2018. Introducing Artificial Music Intelligence to Live Music Shows. https://makezine.com/2018/10/05/introducing-artificial-music-intelligence-to-live-music-shows/
Stinson. 2017. What Happens When Algorithms Design A Concert Hall? The Stunning Elbphilharmonie.
https://www.wired.com/2017/01/happens-algorithms-design-concert-hall-stunning-elbphilharmonie/
Grant. 2017. How To Think About Artificial Intelligence In The Music Industry.
https://www.forbes.com/sites/kristinwestcottgrant/2017/12/10/how-to-think-about-artificial-intelligence-in-the-music-industry/#19e4f4bc7d4a
Marin. 2018. AI-driven data could be the music industry’s best marketing instrument.
AI-driven data could be the music industry’s best marketing instrument
Kokkinis. 2018. Artificial Intelligence in Music Making: is a Jobless Future Ahead of Us?
Artificial Intelligence in Music Making: is a Jobless Future Ahead of Us?
