How AI Technology is Transforming Music Streaming Experience?

We all love Spotify, Gaana, and other music streaming apps, right? But have you ever wondered how Artificial Intelligence plays a huge role in enhancing our experience? Think about this that you favorited a new album or an artist, added a new genre to your list or downloaded a new song. Every move that you make is being acknowledged by the algorithms within to app to prepare and update your preferences.

It has nothing to do with the user capabilities. A user can do manually every single time to explore, add, and save. The only thing is that every time repeating this process consumes more time and that is where technology like Artificial Intelligence helps.

A music streaming app is always a perfect option if you are looking for a companion to perk your mood up. An automated music curation solution for the users is a result of big data, AI & ML technologies. What id I tell you that the music streaming application you use, knows you better! Let’s explore how recommendation models work leveraging these technologies when a music streaming app is built leveraging custom mobile app development.

Types of Music Recommendation Models

There are three sorts of music recommendation models that use three different types of study. the primary is collaborating filtering models, which looks at a user’s behavior on the music streaming app also as what other peoples’ behavior. The second is tongue processing (NLP models), which analyze text, and therefore the third is audio models which take a glance at the raw audio tracks themselves. In most cases, music streaming applications will use a mixture of all three sorts of analysis, as this provides a singular and powerful discovery engine.

Collaborative Filtering

The most common example of collaborative filtering is star-based movie ratings that you simply get on Netflix. It provides you with a basic understanding of which movies you’ll like supported your previous ratings and it provides Netflix with the power to recommend movies/television shows supported by what similar users have enjoyed. For music applications, collaborative filtering is predicated on implicit feedback data, meaning the live stream of your music is counted. This includes what percentage tracks are streamed and extra streaming data like whether a song is saved to a playlist or whether a user visits that specific artist’s page after taking note of one among their songs. Although this is often great, how does it actually work?

A user features a set of track preferences, such as P, R, Q, and T, whereas, another user may have a group of preferences denoted as R, F, P, and Q. The collaborative data shows that both users like Q, R, and P then you almost certainly are both very similar in what you wish. This is often furthered to mention, you’ll probably enjoy what one another hear then you ought to inspect the sole track currently not mentioned within the preference list. So during this instance, for the primary user, it’s F and for the second, it’s track T.

Now the question is, how does a music streaming app do that for many preferences? By using matrix math. Essentially, at the top of the mathematical equation, you get two sorts of vectors, where X is that the user and Y is that the song. once you compare these vectors, you discover which users have similar music tastes and which songs are almost like the present song you’re watching.

NLP & Text Data

The second sort of music recommendation comes from tongue processing which is sourced from text data. this will include news articles, blogs, text through the web, and even metadata. What occurs here is your music streaming applications crawl online, constantly searching for transcription about music, like blogs or song titles, and figures out what people are saying about these songs or artists.

Now because tongue processing allows a computer to know the human speech, it’s ready to see which adjectives are getting used frequently in regard to the songs and artists in question. The data is processed into cultural vectors and top terms. It is given an associated weight that has corresponding importance. Basically, the probability that somebody goes to use that specific term to explain a song, band, genre, or artist. If the probability is high, that piece of music is probably going to be categorized as similar.

Analyzing Raw Audio Tracks with Pinpoint Accuracy

The third sort of music recommendation comes from analyzing raw audio tracks. An example of this is able to be a replacement song coming onto the music application and only getting 50-100 listens but since there are so few filtering against it, this sort of song could find yourself on a discovery playlist alongside popular songs. This is often because raw audio models don’t discriminate against new and popular songs, especially if tongue processing hasn’t picked the track up through text online.

So how do raw audio tracks get analyzed? Through convolutional neural networks that form spectrograms, and by the creation of convolutional layers or “thick and thin” bars showcasing time-frequency representations of audio frames and inputs. After passing through each layer, you’re ready to see what computing statistics are learned across the time of the song, or in layman’s terms the features of the songs. this will include musical time signature, mode, tempo, loudness, and key of the song.

When it comes right down to it, your music streaming application knows you due to the huge amount of knowledge that it stores and analyzes. so as to figure correctly though, audio files, matrices, mathematics, and text must all be analyzed in real-time, applied, and updated through machine learning processes. Yes, a sort of AI power that perfect recommended playlist you tap into on a daily basis.

Wish to know more about how AI & ML can transform your existing business or a brand new idea you have? Contact IndiaNIC’s AI & ML experts now!!