I've always been interested in how the worlds of music and data science interact as a budding musician and data scientist. Data science continues to influence and impact how we think about challenges across numerous sectors as data has become more and more accessible thanks to technology.
Predictive and prescriptive analytics have many uses in the music and entertainment industry, including identifying the top trending song, coming up with the next big hit, and even providing analytics on various streaming platforms like Spotify, Netflix, and YouTube. Today, we'll examine a few data science use cases in the music and entertainment sector. Let's start! Also, do explore the trending data science course in Bangalore, if you are getting started in the field of big data analytics.
Analytics for Audience Engagement
There is now a wealth of information on music consumers because of the growth of internet music streaming services. Distributors like Spotify and Apple Music may provide artists with demographic and geographic insights into their target audience using all this new data.
Moreover, YouTube develops dashboards that show its users' location, viewing time, and demographics. These statistics can be used to respond to important questions like:
- How long did my fans leave my songs on?
- Where do people hear my music played?
- How frequently have you skipped or saved my song?
The best places to market songs, the best times to release songs, etc., may all be decided using these insights. Today's music industry relies on data science and musical skills to predict which songs, genres, or musicians would appeal to a bigger audience.
Creating the Next Massive Hit
You might have noticed when listening to music that many music producers have been writing tracks for different singers that sound similar in an attempt to reproduce great success. Music that sounds alike may have the same genre, lyrics, or beat.
Similar songs are written to familiarize the listener with the genre and increase the likelihood that they would purchase or listen to it.
Creating this kind of music involves applying machine-learning techniques to analyze years' worth of data and pinpoint current patterns. The next big thing is developed through extensive study utilizing data science methods like logistic regression, random forest, and support vector machines.
Another project used artificial intelligence to analyze MIDI music to create brand new tracks from artists or bands with members who have since passed away (using tribute bands to then perform and record the new works), like Amy Winehouse and Nirvana. A song created by Sony's Flow Machine artificial intelligence experiment became well-known (with little human help).
Spotify: Weekly Discover
With Spotify's incredibly clever Discover Weekly function, users may receive a customized playlist based on their listening preferences. Spotify combines machine learning with various data aggregation and sorting techniques to build a recommendation model. There are three main types of recommendation models used by Spotify.
Collaborative Filtering
This technique compares several user-made playlists that contain tunes that are similar to one another. The algorithm scans each playlist, finds songs that seem to be similar, and suggests those songs.
Natural Language Processing (NLP)
Real-time voice and text analysis are done by Spotify using NLP. To build a profile for each song, our algorithm crawls the web in search of any text about the music. The system categorizes each music based on the type of language, theme, and keywords detected in the data.
Convolutional Neural Networks (CNN)
Last but not least, Spotify employs CNN to improve precision and guarantee that less well-known songs are not left out of the model. The waveforms CNN creates from audio files are given important criteria like beats per minute, loudness, major/minor keys, etc. The model looks for songs with similar patterns using these crucial characteristics.
Every week, Spotify builds gorgeously customized playlists for each user using these amazing machine-learning algorithms!
Suggestion Generator on Netflix
I was dubious when Netflix informed me that "Ozark" was a 98% match. I didn't think I would enjoy a program about drug lords and money laundering, but it turns out Netflix knew me better than I did! One of the best shows I've ever seen was Ozark; thanks, Netflix data science team! So how does it function?
Netflix gathers user data, including when a person watched a show, how often they paused it, whether they resumed watching, what they searched for, what kinds of shows they had previously viewed, etc.
Each user has a profile that Netflix has created using this information. Next, similar to Spotify, it uses machine learning strategies like collaborative filtering and neural networks to suggest media that fits that description!
False news and trashy content on YouTube
Fake news is frequently seen on social media sites like Facebook, Instagram, and YouTube. YouTube has used artificial intelligence to highlight false and misleading information.
An AI tool with these skills must first learn how to read and recognize bias, which is extremely difficult. Within the first several days of COVID-19, YouTube eliminated around 11 million videos using AI.
YouTube further uses machine learning algorithms to recognize misleading or objectionable videos. NLP can be used to examine video titles and determine the suitability of the content. The program also looks at user reviews that point out videos with deceptive or click-bait thumbnails. For detailed information on NLP and its techniques, refer to the data science course in Pune.
Conclusion
We've only begun to scratch the surface of what can be done with data science applications in the music and entertainment business, despite this blog exploring a few high-level principles and applications. A question nearly always forms the basis of a sound model. There's a good chance that data science can be applied to improve your customer's experience and maybe even your business if you have a query (and some data!).
