Today’s world is enthralled in the world of music. Be it travelling on a local train in Mumbai, or in a metro in Delhi or in a cab, as a matter of fact, all of us love to get tangled in the serenity of music and escape the reality of the world for a short instance. Some find it therapeutic; some just want to escape the noisy crowd. What helps us to detangle from the real world and entangle ourselves in the world of music are apps such as Spotify, Amazon Music, etc. Did you know that Data Science has reached out its helping hands to these apps as well? Let’s take a look how Data Science makes it easier for the apps to provide us with loops of music.
Data science helps big music companies to analyze trends and predict what the next big hit might be. Companies like Spotify regularly release trends based upon the kind of music their users are listening to. Music companies can take advantage of this data to understand the kind of music which might appeal to a large audience. Another attractive feature of Spotify is Discover Weekly, where the listeners receive a personalized playlist based on their taste and listening history. Spotify uses machine learning along with various data aggregation and sorting methods to create a recommendation model. Spotify primarily uses three forms of recommendation model:
Collaborative Filtering:
This process includes comparing multiple user-created playlists that have similar songs. The algorithm goes through each playlist and identifies other songs that appear to be alike and recommends those songs.
2. Natural Language Processing (NLP):
Spotify uses NLP to analyze speech and text in real-time. This algorithm scrapes the web to find any text related to the music to create a profile for each song. Based on the data found, the algorithm classifies each song according to the type of language, theme, and keywords.
3. Convolutional Neural Networks (CNN):
Spotify uses this feature to increase the accuracy and ensure less-popular songs are not neglected in the model. CNN converts audio files to waveform, which are assigned key parameters such as beats per minute, volume, major/minor keys, etc. Using these key parameters, the model tries to match other songs with similar patterns.
Using these awesome machine learning models, Spotify creates beautifully tailored playlists for each listener every week!
In similar lines of prediction Spotify managed to predict the Grammys in 2013. They made this possible by breaking down the user’s listening habit by considering the song and the album that was being streamed to determine the popularity of the music. By the end of this experiment, 4 out of 6 of their predictions turned out to be right.
A huge amount of research also goes into producing the next big hit using data science techniques such as logistic regression, random forest, and support vector machine. Sony famously released a song built from their Flow Machine's artificial intelligence project. Artificial intelligence also analyses MIDI music to create brand new tracks from artists or bands with members that have since passed away (using tribute bands to then perform and record the new creations), such as Nirvana and Amy Winehouse.
This acts as a proof to say that Data Science and Machine learning venture in every field possible!
By,
Aarathi Iyer
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