We See You — Machine Learning and Its Growing Effect on the Music Industry

17 February 2017
By Sergey Bludov, VP, Partner at DataArt

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There are more than enough articles concerning Machine Learning (ML) and Artificial Intelligence (AI) these days. AI is going to make our life easier, it is going to have a beneficial impact on how we conduct business and it may even replace certain things that we do not expect at this moment and time. One thing is sure: the ML/AI movement is on the fast track to broad adoption that will affect almost every industry.

We already see AI creeping into our lives. Siri and Alexa, smart homes and predictive analytics are being widely used to find out what we need, when we need it and how to get it. Per Tractica, the market for enterprise AI systems will increase to $11.1 B by 2024. 

In the music industry, companies like Shazam have applied machine learning and AI and to a method called acoustic fingerprinting by developing audio fingerprinting algorithms. In basic terms, Shazam converts a melody into a stream of bits and compares it to the results of the same algorithm applied to a big database of songs that help the listener discover music that is playing around them.

Spotify has already implemented machine learning that happens behind the scenes. It’s all about recommendations based upon your listening habits. While discover weekly is not quite there yet, it’s a good start towards better conversion. They are also considering deep learning. Imagine being sent a track to listen to, that you will invariably enjoy, even before you request it. It’s quite amazing actually.

Record Labels, producers, and artists use ML to make more intelligent and profitable business decisions on every level. Whether it is finding ways to use data to cater offers, increase conversions, create hit music, drive collaborations and partnerships, develop artist apps and emoji’s, schedule events and concert dates, or simply sell more music, data is changing how the music industry relates to fans and impacts merchandising, engagement, relationships, and experiences.

At the end of the day, data is the fuel for any machine learning algorithm and machine learning technologies are only as good as the data powering them. Many companies have reached the limit regarding the amount of data their algorithms can analyze. Data quality is very important due to the current limitations of AI being able to come to the same conclusions as an average human. AI requires a serious amount of data, thousands of times more than humans do in order to reflect the same outcome. Therefore, the next obstacle in the bright future of AI and its numerous areas of application would be the question of how much information are we willing to feed it? Once we dealt with that, who’s going to build the solutions that actually aggregate/categorize and finally feed this data into AI? 

There are essentially 3 buckets we cannot fill without jeopardizing at least one: data security, data accessibility and data efficiency, all of which are crucial for AI success. It is also how the data is organized and used that offers significant opportunities for technology companies. 

The upcoming SXSW Conference is devoting a number of sessions to AI and its industry applicability. If you are attending SXSW, make sure you join in on the discussion. 

Below are a few of the sessions you may want to attend.

Are you planning to attend? Please feel free to reach out and schedule a meeting. We’ll see you there!


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